Several lines of evidence implicate the prefrontal cortex in learning but there is little evidence from studies of human lesion patients to demonstrate the critical role of this structure. To this end, we tested patients with lesions of the frontal lobe (n = 36) and healthy controls (n = 35) on two learning tasks: the weather prediction task…

Using a conditioned suppression task, we investigated simultaneous (XA-/A+) vs. sequential (X [right arrow] A-/A+) Feature Negative (FN) discriminationlearning in humans. We expected the simultaneous discrimination to result in X (or alternatively the XA configuration) becoming an inhibitor acting directly on the US, and the sequential…

Examined the learning performance of 192 fourth-, fifth-, and sixth-grade children on either a two or four choice simultaneous color discrimination task. Compared the use of verbal reinforcement and/or punishment, under conditions of either complete or incomplete instructions. (Author/SDH)

In machine learning and computer vision, input images are often filtered to increase data discriminability. In some situations, however, one may wish to purposely decrease discriminability of one classification task (a "distractor" task), while simultaneously preserving information relevant to another (the task-of-interest): For example, it may be important to mask the identity of persons contained in face images before submitting them to a crowdsourcing site (e.g., Mechanical Turk) when labeling them for certain facial attributes. Another example is inter-dataset generalization: when training on a dataset with a particular covariance structure among multiple attributes, it may be useful to suppress one attribute while preserving another so that a trained classifier does not learn spurious correlations between attributes. In this paper we present an algorithm that finds optimal filters to give high discriminability to one task while simultaneously giving low discriminability to a distractor task. We present r...

Generalization gradients have been investigated widely in animal conditioning experiments, but much less so in human predictive learning tasks. Here, we apply the experimental design of a recent study on conditioned fear generalization in humans (Lissek et al., 2008) to a predictive learning task, and examine the effects of a number of relevant…

Full Text Available Although visual feature leaning has been well studied, we still know little about the mechanisms of perceptual learning of complex object. Here, human perceptual learning in discrimination of in-depth orientation of face view was studied using psychophysics, EEG and fMRI. We trained subjects to discriminate face orientations around a face view (i.e. 30° over eight daily sessions, which resulted in a significant improvement in sensitivity to the face view orientation. This improved sensitivity was highly specific to the trained orientation and persisted up to six months. Different from perceptual learning of simple visual features, this orientation-specific learning effect could completely transfer across changes in face size, visual field and face identity. A complete transfer also occurred between two partial face images that were mutually exclusive but constituted a complete face. However, the transfer of the learning effect between upright and inverted faces and between a face and a paperclip object was very weak. Before and after training, we measured EEG and fMRI BOLD signals responding to both the trained and the untrained face views. Analyses of ERPs and induced gamma activity showed that face view discrimination training led to a larger reduction of N170 latency at the left occipital-temporal area and a concurrent larger decrease of induced gamma activity at the left frontal area with the trained face view, compared with the untrained ones. BOLD signal amplitude and MVPA analyses showed that, in face-selective cortical areas, training did not lead to a significant amplitude change, but induced a more reliable spatial pattern of neural activity in the left FFA. These results suggest that the visual system had learned how to compute face orientation from face configural information more accurately and that a large amount of plastic changes took place at a level of higher visual processing where size-, location-, and identity

Reinforcement learning theory has generated substantial interest in neurobiology, particularly because of the resemblance between phasic dopamine and reward prediction errors. Actor-critic theories have been adapted to account for the functions of the striatum, with parts of the dorsal striatum equated to the actor. Here, we specifically test whether the human dorsal striatum--as predicted by an actor-critic instantiation--is used on a trial-to-trial basis at the time of choice to choose in accordance with reinforcement learning theory, as opposed to a competing strategy: the gambler's fallacy. Using a partial-brain functional magnetic resonance imaging scanning protocol focused on the striatum and other ventral brain areas, we found that the dorsal striatum is more active when choosing consistent with reinforcement learning compared with the competing strategy. Moreover, an overlapping area of dorsal striatum along with the ventral striatum was found to be correlated with reward prediction errors at the time of outcome, as predicted by the actor-critic framework. These findings suggest that the same region of dorsal striatum involved in learning stimulus-response associations may contribute to the control of behavior during choice, thereby using those learned associations. Intriguingly, neither reinforcement learning nor the gambler's fallacy conformed to the optimal choice strategy on the specific decision-making task we used. Thus, the dorsal striatum may contribute to the control of behavior according to reinforcement learning even when the prescriptions of such an algorithm are suboptimal in terms of maximizing future rewards.

In this book, we introduce the background and mainstream methods of probabilistic modeling and discriminative parameter optimization for speech recognition. The specific models treated in depth include the widely used exponential-family distributions and the hidden Markov model. A detailed study is presented on unifying the common objective functions for discriminativelearning in speech recognition, namely maximum mutual information (MMI), minimum classification error, and minimum phone/word error. The unification is presented, with rigorous mathematical analysis, in a common rational-functio

We consider the problem of predicting a sequence of real-valued multivariate states that are correlated by some unknown dynamics, from a given measurement sequence. Although dynamic systems such as the State-Space Models are popular probabilistic models for the problem, their joint modeling of states and observations, as well as the traditional generative learning by maximizing a joint likelihood may not be optimal for the ultimate prediction goal. In this paper, we suggest two novel discriminative approaches to the dynamic state prediction: 1) learning generative state-space models with discriminative objectives and 2) developing an undirected conditional model. These approaches are motivated by the success of recent discriminative approaches to the structured output classification in discrete-state domains, namely, discriminative training of Hidden Markov Models and Conditional Random Fields (CRFs). Extending CRFs to real multivariate state domains generally entails imposing density integrability constraints on the CRF parameter space, which can make the parameter learning difficult. We introduce an efficient convex learning algorithm to handle this task. Experiments on several problem domains, including human motion and robot-arm state estimation, indicate that the proposed approaches yield high prediction accuracy comparable to or better than state-of-the-art methods.

Using combined psychophysics and event-related potentials (ERPs), we investigated the effect of perceptual learning on face gender discrimination and probe the neural correlates of the learning effect. Human subjects were trained to perform a gender discrimination task with male or female faces. Before and after training, they were tested with the trained faces and other faces with the same and opposite genders. ERPs responding to these faces were recorded. Psychophysical results showed that training significantly improved subjects' discrimination performance and the improvement was specific to the trained gender, as well as to the trained identities. The training effect indicates that learning occurs at two levels-the category level (gender) and the exemplar level (identity). ERP analyses showed that the gender and identity learning was associated with the N170 latency reduction at the left occipital-temporal area and the N170 amplitude reduction at the right occipital-temporal area, respectively. These findings provide evidence for the facilitation model and the sharpening model on neuronal plasticity from visual experience, suggesting a faster processing speed and a sparser representation of face induced by perceptual learning.

Our research purpose is to build an artificial neural network with an excellent color discrimination capability like human being on a computer. In this study, we built the network, which was trained to learn 10 colors with different hues in the Munsell color system. Then, we examined the response of the trained network when the network was interrogated about 10 non-learned colors. The network showed a good color discrimination capability, close to that of human being.

We present an ART-based neural network model (adapted from [2]) of the development of discrimination-shift learning that models the trial-by-trial learning process in great detail. In agreement with the results of human participants (4-20 years of age) in [1] the model revealed two distinct learning

High school students analyze real-life case studies, taken from the files of the Ontario Human Rights Commission, to learn about the effects of prejudice, stereotyping, and discrimination with regard to native people in Canada. (RM)

Full Text Available The middle temporal area of the extrastriate visual cortex (area MT is integral to motion perception and is thought to play a key role in the perceptual learning of motion tasks. We have previously found, however, that perceptual learning of a motion discrimination task is possible even when the training stimulus contains locally balanced, motion opponent signals that putatively suppress the response of MT. Assuming at least partial suppression of MT, possible explanations for this learning are that 1 training made MT more responsive by reducing motion opponency, 2 MT remained suppressed and alternative visual areas such as V1 enabled learning and/or 3 suppression of MT increased with training, possibly to reduce noise. Here we used fMRI to test these possibilities. We first confirmed that the motion opponent stimulus did indeed suppress the BOLD response within hMT+ compared to an almost identical stimulus without locally balanced motion signals. We then trained participants on motion opponent or non-opponent stimuli. Training with the motion opponent stimulus reduced the BOLD response within hMT+ and greater reductions in BOLD response were correlated with greater amounts of learning. The opposite relationship between BOLD and behaviour was found at V1 for the group trained on the motion-opponent stimulus and at both V1 and hMT+ for the group trained on the non-opponent motion stimulus. As the average response of many cells within MT to motion opponent stimuli is the same as their response to non-directional flickering noise, the reduced activation of hMT+ after training may reflect noise reduction.

Visual discrimination tasks are commonly used to assess visual learning and memory in non-human animals. The current experiments explored the suitability of an iPad (Apple, Cupertino, California), as a low-cost alternative touchscreen for visual discrimination tasks. In Experiment 1, rats were trained with patterned black-and-white stimuli in a successive non-match to sample procedure. Rats successfully interacted with the iPad but failed to learn to withhold responding on trials in which the sample matched the comparison. Experiment 2 used the same patterned stimuli, but the procedure was simplified to a successive discrimination procedure and we explored the use of procedures known to facilitate discriminationlearning. Rats that received training with differential outcomes and a differential reinforcement of other behavior schedule successfully acquired the task. In Experiment 3, the same rats were tested in a simultaneous discrimination task and we explored the use of a correction and non-correction method during acquisition. Rats that failed to learn the discrimination in the previous experiment, improved while trained with the correction method. These experiments support the use of the iPad in visual discrimination tasks and inform future studies investigating learning and memory within a touchscreen-equipped (iPad or other) apparatus.

In this paper, a discriminative structured dictionary learning algorithm is presented. To enhance the dictionary’s discriminative power, the reconstruction error, classification error and inhomogeneous representation error are integrated into the objective function. The proposed approach learns a single structured dictionary and a linear classifier jointly. The learned dictionary encourages the samples from the same class to have similar sparse codes, and the samples from different classes to have dissimilar sparse codes. The solution to the objective function is achieved by employing a feature-sign search algorithm and Lagrange dual method. Experimental results on three public databases demonstrate that the proposed approach outperforms several recently proposed dictionary learning techniques for classification.

Full Text Available Perceptual training is generally assumed to improve perception by modifying the encoding or decoding of sensory information. However, this assumption is incompatible with recent demonstrations that transfer of learning can be enhanced by across-trial variation of training stimuli or task. Here we present three lines of evidence from healthy adults in support of the idea that the enhanced transfer of auditory discriminationlearning is mediated by working memory (WM. First, the ability to discriminate small differences in tone frequency or duration was correlated with WM measured with a tone n-back task. Second, training frequency discrimination around a variable frequency transferred to and from WM learning, but training around a fixed frequency did not. The transfer of learning in both directions was correlated with a reduction of the influence of stimulus variation in the discrimination task, linking WM and its improvement to across-trial stimulus interaction in auditory discrimination. Third, while WM training transferred broadly to other WM and auditory discrimination tasks, variable-frequency training on duration discrimination did not improve WM, indicating that stimulus variation challenges and trains WM only if the task demands stimulus updating in the varied dimension. The results provide empirical evidence as well as a theoretic framework for interactions between cognitive and sensory plasticity during perceptual experience.

Full Text Available Abstract Background The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of different and well-adapted models has been developed, but only little attention has been payed to the development of different and similarly well-adapted learning principles. Only recently it was noticed that discriminativelearning principles can be superior over generative ones in diverse bioinformatics applications, too. Results Here, we propose a generalization of generative and discriminativelearning principles containing the maximum likelihood, maximum a posteriori, maximum conditional likelihood, maximum supervised posterior, generative-discriminative trade-off, and penalized generative-discriminative trade-off learning principles as special cases, and we illustrate its efficacy for the recognition of vertebrate transcription factor binding sites. Conclusions We find that the proposed learning principle helps to improve the recognition of transcription factor binding sites, enabling better computational approaches for extracting as much information as possible from valuable wet-lab data. We make all implementations available in the open-source library Jstacs so that this learning principle can be easily applied to other classification problems in the field of genome and epigenome analysis.

In recent years, sparse representation has been widely used in object recognition applications. How to learn the dictionary is a key issue to sparse representation. A popular method is to use l1 norm as the sparsity measurement of representation coefficients for dictionary learning. However, the l1 norm treats each atom in the dictionary independently, so the learned dictionary cannot well capture the multisubspaces structural information of the data. In addition, the learned subdictionary for each class usually shares some common atoms, which weakens the discriminative ability of the reconstruction error of each subdictionary. This paper presents a new dictionary learning model to improve sparse representation for image classification, which targets at learning a class-specific subdictionary for each class and a common subdictionary shared by all classes. The model is composed of a discriminative fidelity, a weighted group sparse constraint, and a subdictionary incoherence term. The discriminative fidelity encourages each class-specific subdictionary to sparsely represent the samples in the corresponding class. The weighted group sparse constraint term aims at capturing the structural information of the data. The subdictionary incoherence term is to make all subdictionaries independent as much as possible. Because the common subdictionary represents features shared by all classes, we only use the reconstruction error of each class-specific subdictionary for classification. Extensive experiments are conducted on several public image databases, and the experimental results demonstrate the power of the proposed method, compared with the state-of-the-arts.

Full Text Available In recent years, imbalanced learning problem has attracted more and more attentions from both academia and industry, and the problem is concerned with the performance of learning algorithms in the presence of data with severe class distribution skews. In this paper, we apply the well-known statistical model logistic discrimination to this problem and propose a novel method to improve its performance. To fully consider the class imbalance, we design a new cost function which takes into account the accuracies of both positive class and negative class as well as the precision of positive class. Unlike traditional logistic discrimination, the proposed method learns its parameters by maximizing the proposed cost function. Experimental results show that, compared with other state-of-the-art methods, the proposed one shows significantly better performance on measures of recall, g-mean, f-measure, AUC, and accuracy.

We propose a Bayesian approach to learndiscriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

The results from five experiments are considered in relation to two of Spence's (1937, 1938) proposals concerning discriminationlearning. In Experiments 1 and 2, we investigated whether his ideas about the interaction between excitatory and inhibitory generalization gradients can be used to understand how animals solve a complex patterning discrimination. The results supported a development of his proposals as put forward by Pearce (1994), provided a modification was made to Pearce's rulefor determining the shape ofthe generalization gradient. In Experiments 3, 4, and 5, we examined whether animals would pay more attention to stimuli that are relevant, rather than irrelevant, to the solution of a discrimination. The results supported this proposal for stimuli comprising visual patterns, but not for those comprising plain colors. The results also indicated that change of attention was a consequence of preliminary receptor-exposure acts, as envisaged by Spence, and not of more central changes in attention.

Orientation selective neurons in the primary visual cortex typically respond to a range of orientations that covers 20 degrees or more, while in psychophysical experiments, orientation bandwidth is often clearly narrower. Here, we measure the orientation specificity of perceptual learning for vernier discriminations. More than 70 observers, in separate groups, practiced a vernier discrimination task with a constant stimulus orientation. After a 1h session of training, the vernier was rotated by 2 degrees, 4 degrees, 10 degrees, 20 degrees, 45 degrees or 90 degrees. Improvement through training in the first session transferred to the second session (tested on the next day) up to 10 degrees of stimulus rotation. We found no transfer for rotations of 20 degrees, 45 degrees and 90 degrees. Hence, the orientation half-bandwidth of perceptual learning is around 15 degrees, leading to a bandwidth of 30 degrees and corresponding to that of single neurons in early visual cortices, while being narrower than that in higher cortical areas.

The question of whether animals have emotions and respond to the emotional expressions of others has become a focus of research in the last decade [1-9]. However, to date, no study has convincingly shown that animals discriminate between emotional expressions of heterospecifics, excluding the possibility that they respond to simple cues. Here, we show that dogs use the emotion of a heterospecific as a discriminative cue. After learning to discriminate between happy and angry human faces in 15 picture pairs, whereby for one group only the upper halves of the faces were shown and for the other group only the lower halves of the faces were shown, dogs were tested with four types of probe trials: (1) the same half of the faces as in the training but of novel faces, (2) the other half of the faces used in training, (3) the other half of novel faces, and (4) the left half of the faces used in training. We found that dogs for which the happy faces were rewarded learned the discrimination more quickly than dogs for which the angry faces were rewarded. This would be predicted if the dogs recognized an angry face as an aversive stimulus. Furthermore, the dogs performed significantly above chance level in all four probe conditions and thus transferred the training contingency to novel stimuli that shared with the training set only the emotional expression as a distinguishing feature. We conclude that the dogs used their memories of real emotional human faces to accomplish the discrimination task.

Discrimination of facial features degrades with stimulus rotation (e.g., the "Margaret Thatcher" effect). Thirty-two observers learned to discriminate between two upright, or two inverted, faces. Images, erect and rotated by +/-45deg, +/-90deg, +/-135deg and 180deg about the line of sight, were presented on a computer screen. Initial discriminative reaction times increased with stimulus rotation only for observers who learned the upright faces. Orientation during learning is critical in identifying faces subsequently seen at different orientations.

This study investigated how infant pigtailed macaque monkeys performed on two separate learning assessments, two-object discrimination/reversal and Hamilton search learning. Although the learning tasks have been tested on several species, including non-human primates, there have been no normative

This study investigated how infant pigtailed macaque monkeys performed on two separate learning assessments, two-object discrimination/reversal and Hamilton search learning. Although the learning tasks have been tested on several species, including non-human primates, there have been no normative re

Full Text Available Weather recognition based on outdoor images is a brand-new and challenging subject, which is widely required in many fields. This paper presents a novel framework for recognizing different weather conditions. Compared with other algorithms, the proposed method possesses the following advantages. Firstly, our method extracts both visual appearance features of the sky region and physical characteristics features of the nonsky region in images. Thus, the extracted features are more comprehensive than some of the existing methods in which only the features of sky region are considered. Secondly, unlike other methods which used the traditional classifiers (e.g., SVM and K-NN, we use discriminative dictionary learning as the classification model for weather, which could address the limitations of previous works. Moreover, the active learning procedure is introduced into dictionary learning to avoid requiring a large number of labeled samples to train the classification model for achieving good performance of weather recognition. Experiments and comparisons are performed on two datasets to verify the effectiveness of the proposed method.

The relationship between employer learning and statistical discrimination was explored through a statistical analysis that included a test for statistical discrimination or "rational" stereotyping in environments where agents learn over time. The test is used to study the working hypothesis that, because firms have only limited information about…

Visual deprivation may lead to enhanced performance in other sensory modalities. Whether this is the case in the tactile modality is controversial and may depend upon specific training and experience. We compared the performance of sighted subjects on a Braille character discrimination task to that of normal individuals blindfolded for a period of five days. Some participants in each group (blindfolded and sighted) received intensive Braille training to offset the effects of experience. Blindfolded subjects performed better than sighted subjects in the Braille discrimination task, irrespective of tactile training. For the left index finger, which had not been used in the formal Braille classes, blindfolding had no effect on performance while subjects who underwent tactile training outperformed non-stimulated participants. These results suggest that visual deprivation speeds up Braille learning and may be associated with behaviorally relevant neuroplastic changes.

This correspondence presents a two-stage classification learning algorithm. The first stage approximates the class-conditional distribution of a discrete space using a separate mixture model, and the second stage investigates the class posterior probabilities by training a network. The first stage explores the generative information that is inherent in each class by using the Chow-Liu (CL) method, which approximates high-dimensional probability with a tree structure, namely, a dependence tree, whereas the second stage concentrates on discriminativelearning to distinguish between classes. The resulting learning algorithm integrates the advantages of both generative learning and discriminativelearning. Because it uses CL dependence-tree estimation, we call our algorithm CL-Net. Empirical tests indicate that the proposed learning algorithm makes significant improvements when compared with the related classifiers that are constructed by either generative learning or discriminativelearning.

Human Machine Learning Symbiosis is a cooperative system where both the human learner and the machine learner learn from each other to create an effective and efficient learning environment adapted to the needs of the human learner. Such a system can be used in online learning modules so that the modules adapt to each learner's learning state both…

Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary (DDD) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First, minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second, linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third, instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.

Reinforcement learning tasks are often used to assess participants’ tendency to learn more from the positive or more from the negative consequences of one’s action. However, this assessment often requires comparison in learning performance across different task conditions, which may differ in the relative salience or discriminability of the stimuli associated with more and less rewarding outcomes, respectively. To address this issue, in a first set of studies, participants were subjected to two versions of a common probabilistic learning task. The two versions differed with respect to the stimulus (Hiragana) characters associated with reward probability. The assignment of character to reward probability was fixed within version but reversed between versions. We found that performance was highly influenced by task version, which could be explained by the relative perceptual discriminability of characters assigned to high or low reward probabilities, as assessed by a separate discrimination experiment. Participants were more reliable in selecting rewarding characters that were more discriminable, leading to differences in learning curves and their sensitivity to reward probability. This difference in experienced reinforcement history was accompanied by performance biases in a test phase assessing ability to learn from positive vs. negative outcomes. In a subsequent large-scale web-based experiment, this impact of task version on learning and test measures was replicated and extended. Collectively, these findings imply a key role for perceptual factors in guiding reward learning and underscore the need to control stimulus discriminability when making inferences about individual differences in reinforcement learning. PMID:28481915

Reinforcement learning tasks are often used to assess participants' tendency to learn more from the positive or more from the negative consequences of one's action. However, this assessment often requires comparison in learning performance across different task conditions, which may differ in the relative salience or discriminability of the stimuli associated with more and less rewarding outcomes, respectively. To address this issue, in a first set of studies, participants were subjected to two versions of a common probabilistic learning task. The two versions differed with respect to the stimulus (Hiragana) characters associated with reward probability. The assignment of character to reward probability was fixed within version but reversed between versions. We found that performance was highly influenced by task version, which could be explained by the relative perceptual discriminability of characters assigned to high or low reward probabilities, as assessed by a separate discrimination experiment. Participants were more reliable in selecting rewarding characters that were more discriminable, leading to differences in learning curves and their sensitivity to reward probability. This difference in experienced reinforcement history was accompanied by performance biases in a test phase assessing ability to learn from positive vs. negative outcomes. In a subsequent large-scale web-based experiment, this impact of task version on learning and test measures was replicated and extended. Collectively, these findings imply a key role for perceptual factors in guiding reward learning and underscore the need to control stimulus discriminability when making inferences about individual differences in reinforcement learning.

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separab...

Learning styles in capuchin monkeys were assessed with a computerized reversal-learning task called the mediational paradigm. First, monkeys were trained to respond with 90% accuracy on a two-choice discrimination (A+B-). Then the authors examined differences in performance on three different types of reversal trials (A-B+, A-C+, B+C-), each of…

We suggest and investigate the use of Generalized Matrix Relevance Learning (GMLVQ) in the context of discriminative visualization. This prototype-based, supervised learning scheme parameterizes an adaptive distance measure in terms of a matrix of relevance factors. By means of a few benchmark

We consider perceptual learning: experience-induced changes in the way perceivers extract information. Often neglected in scientific accounts of learning and in instruction, perceptual learning is a fundamental contributor to human expertise and is crucial in domains where humans show remarkable levels of attainment, such as language, chess, music, and mathematics. In Section 2, we give a brief history and discuss the relation of perceptual learning to other forms of learning. We consider in Section 3 several specific phenomena, illustrating the scope and characteristics of perceptual learning, including both discovery and fluency effects. We describe abstract perceptual learning, in which structural relationships are discovered and recognized in novel instances that do not share constituent elements or basic features. In Section 4, we consider primary concepts that have been used to explain and model perceptual learning, including receptive field change, selection, and relational recoding. In Section 5, we consider the scope of perceptual learning, contrasting recent research, focused on simple sensory discriminations, with earlier work that emphasized extraction of invariance from varied instances in more complex tasks. Contrary to some recent views, we argue that perceptual learning should not be confined to changes in early sensory analyzers. Phenomena at various levels, we suggest, can be unified by models that emphasize discovery and selection of relevant information. In a final section, we consider the potential role of perceptual learning in educational settings. Most instruction emphasizes facts and procedures that can be verbalized, whereas expertise depends heavily on implicit pattern recognition and selective extraction skills acquired through perceptual learning. We consider reasons why perceptual learning has not been systematically addressed in traditional instruction, and we describe recent successful efforts to create a technology of perceptual

Full Text Available The cognitive and neural mechanisms for recognizing and categorizing behavior are not well understood in non-human animals. In the current experiments, pigeons and humanslearned to categorize two non-repeating, complex human behaviors ("martial arts" vs. "Indian dance". Using multiple video exemplars of a digital human model, pigeons discriminated these behaviors in a go/no-go task and humans in a choice task. Experiment 1 found that pigeons already experienced with discriminating the locomotive actions of digital animals acquired the discrimination more rapidly when action information was available than when only pose information was available. Experiments 2 and 3 found this same dynamic superiority effect with naïve pigeons and human participants. Both species used the same combination of immediately available static pose information and more slowly perceived dynamic action cues to discriminate the behavioral categories. Theories based on generalized visual mechanisms, as opposed to embodied, species-specific action networks, offer a parsimonious account of how these different animals recognize behavior across and within species.

The ability to successfully discriminate between multiple potentially relevant source analogs when solving new problems is crucial to proficiency in a mathematics domain. Experimental findings in two different mathematical contexts demonstrate that providing cues to support comparative reasoning during an initial instructional analogy, relative to…

Pigeons learned two concurrent simultaneous discriminations in which the S- for one served as the S+ for the other. When all correct choices were reinforced, accuracy on the former (positive vs. ambiguous-cue or PA) discrimination was lower than on the latter (negative vs. ambiguous-cue or NA) discrimination. When correct choices on the PA discrimination were intermittently reinforced, however, pigeons chose the S- more often than the S+ on those trials. By contrast, intermittently reinforcing correct choices on the NA discrimination did not affect NA-trial accuracy but yielded higher PA-trial accuracy relative to continuous reinforcement. Together with a separate preference assessment, these results indicate that value transfer, in which some of the positive value accrued by an S+ transfers to its companion S-, contributes to ambiguous-cue performances.

In visual saliency estimation, one of the most challenging tasks is to distinguish targets and distractors that share certain visual attributes. With the observation that such targets and distractors can sometimes be easily separated when projected to specific subspaces, we propose to estimate image saliency by learning a set of discriminative subspaces that perform the best in popping out targets and suppressing distractors. Toward this end, we first conduct principal component analysis on massive randomly selected image patches. The principal components, which correspond to the largest eigenvalues, are selected to construct candidate subspaces since they often demonstrate impressive abilities to separate targets and distractors. By projecting images onto various subspaces, we further characterize each image patch by its contrasts against randomly selected neighboring and peripheral regions. In this manner, the probable targets often have the highest responses, while the responses at background regions become very low. Based on such random contrasts, an optimization framework with pairwise binary terms is adopted to learn the saliency model that best separates salient targets and distractors by optimally integrating the cues from various subspaces. Experimental results on two public benchmarks show that the proposed approach outperforms 16 state-of-the-art methods in human fixation prediction.

We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.

A system for discrimination of stop consonants has been designed on the basis of studies of auditory physiology and psychophysics. The system consists of a one-third octave filter bank as an approximation to auditory tuning curves, a bank of high speed, wide dynamic range envelope detectors, a logarithmic amplifier, and a digital computer for analysis and display. Features, chosen on the basis of psychophysical experiments, are then abstracted, and fed to a discriminant analysis program which decides on the most probable phomene. Discrimination accuracy of about 77% for stop consonants in initial position has been achieved, with a 15-speaker data set.

Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods.

Discrimination of human and nonhuman blood is crucial for import-export ports and inspection and quarantine departments. Current methods are usually destructive, complicated and time-consuming. We had previously demonstrated that visible diffuse reflectance spectroscopy combining PLS-DA method can successfully realize human blood discrimination. In that research, the spectra were measured with the fiber probe under the surface of blood samples. However, open sampling may pollute the blood samples. Virulence factors in blood samples can also endanger inspectors. In this paper, we explored the classification effect with the blood samples measured in the original containers-vacuum blood vessel. Furthermore, we studied the impact of different conditions of blood samples, such as coagulation and hemolysis, on the prediction ability of the discrimination model. The calibration model built with blood samples in different conditions displayed a satisfactory prediction result. This research demonstrated that visible and near-infrared diffuse reflectance spectroscopy method was potential for noncontact discrimination of human blood.

Adult male bobwhite quail (Colinus virginianus) were fed a diet containing 0, 25 or 100 ppm paraquat dichloride. After 60 d on treated diets, discriminationlearning was evaluated with acquisition and reversal tests. The three groups performed similarly on these tests. Dose-related histopathological lesions were not found in liver, kidney or lung tissues

Ten autistic children and 10 normal nursery school children, matched for mean developmental age, were presented with figure stimuli and had variable irrelevant cues in two-choice simultaneous discriminationlearning. Performance of the autistic group did not vary as a function of irrelevant variability, a result attributed to poor performance of…

We present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-di

This article delineates legal protections prohibiting employment discrimination of individuals with learning disabilities. The Rehabilitation Act of 1973 (Title V) and Americans with Disabilities Act (Title I) are described, fundamental concepts are discussed, and strategies are presented for ensuring that these laws are relied upon appropriately…

Accurate identification and understanding informative feature is important for early Alzheimer's disease (AD) prognosis and diagnosis. In this paper, we propose a novel discriminative sparse learning method with relational regularization to jointly predict the clinical score and classify AD disease stages using multimodal features. Specifically, we apply a discriminativelearning technique to expand the class-specific difference and include geometric information for effective feature selection. In addition, two kind of relational information are incorporated to explore the intrinsic relationships among features and training subjects in terms of similarity learning. We map the original feature into the target space to identify the informative and predictive features by sparse learning technique. A unique loss function is designed to include both discriminativelearning and relational regularization methods. Experimental results based on a total of 805 subjects [including 226 AD patients, 393 mild cognitive impairment (MCI) subjects, and 186 normal controls (NCs)] from AD neuroimaging initiative database show that the proposed method can obtain a classification accuracy of 94.68% for AD versus NC, 80.32% for MCI versus NC, and 74.58% for progressive MCI versus stable MCI, respectively. In addition, we achieve remarkable performance for the clinical scores prediction and classification label identification, which has efficacy for AD disease diagnosis and prognosis. The algorithm comparison demonstrates the effectiveness of the introduced learning techniques and superiority over the state-of-the-arts methods.

Associating schizophrenia with disrupted functional connectivity is a central idea in schizophrenia research. However, identifying neuroimaging-based features that can serve as reliable "statistical biomarkers" of the disease remains a challenging open problem. We argue that generalization accuracy and stability of candidate features ("biomarkers") must be used as additional criteria on top of standard significance tests in order to discover more robust biomarkers. Generalization accuracy refers to the utility of biomarkers for making predictions about individuals, for example discriminating between patients and controls, in novel datasets. Feature stability refers to the reproducibility of the candidate features across different datasets. Here, we extracted functional connectivity network features from fMRI data at both high-resolution (voxel-level) and a spatially down-sampled lower-resolution ("supervoxel" level). At the supervoxel level, we used whole-brain network links, while at the voxel level, due to the intractably large number of features, we sampled a subset of them. We compared statistical significance, stability and discriminative utility of both feature types in a multi-site fMRI dataset, composed of schizophrenia patients and healthy controls. For both feature types, a considerable fraction of features showed significant differences between the two groups. Also, both feature types were similarly stable across multiple data subsets. However, the whole-brain supervoxel functional connectivity features showed a higher cross-validation classification accuracy of 78.7% vs. 72.4% for the voxel-level features. Cross-site variability and heterogeneity in the patient samples in the multi-site FBIRN dataset made the task more challenging compared to single-site studies. The use of the above methodology in combination with the fully data-driven approach using the whole brain information have the potential to shed light on "biomarker discovery" in schizophrenia.

Power system disturbances are inherently complex and can be attributed to a wide range of sources, including both natural and man-made events. Currently, the power system operators are heavily relied on to make decisions regarding the causes of experienced disturbances and the appropriate course of action as a response. In the case of cyber-attacks against a power system, human judgment is less certain since there is an overt attempt to disguise the attack and deceive the operators as to the true state of the system. To enable the human decision maker, we explore the viability of machine learning as a means for discriminating types of power system disturbances, and focus specifically on detecting cyber-attacks where deception is a core tenet of the event. We evaluate various machine learning methods as disturbance discriminators and discuss the practical implications for deploying machine learning systems as an enhancement to existing power system architectures.

We tested pigeons' acquisition of a conditional discrimination task between colored grating stimuli that included choosing 1 of 2 response keys, which either appeared as white keys to the left and right of the discriminative stimulus, or were replicas of the stimulus. Pigeons failed to acquire the discrimination when the response keys were white disks but succeeded when directly responding to a replica of the stimulus. These results highlight how conditioning processes shape learning in pigeons: The results can be accounted for by supposing that, when pigeons were allowed to respond directly toward the stimulus, learning was guided by classical conditioning, but that responding to white keys demanded instrumental learning, which impaired task acquisition for pigeons. In contrast, humans completing the same paradigm showed no differential learning success depending on whether figure or position indicated the correct key. However, only participants who could state the underlying discrimination rule acquired the task, which implies that human performance in this situation relied on the deduction and application of task rules instead of associative processes.

Animals were trained to discriminate a relatively low dose of the octapeptide cholecystokinin (CCK) from distilled water within the conditioned taste aversion baseline of drug discriminationlearning. Specifically, rats were injected with CCK (5.6 micrograms/kg) prior to the presentation of saccharin-LiCl pairings and with the CCK vehicle prior to the presentation of saccharin alone. After 10 conditioning trials (40 days), subjects acquired the discrimination, avoiding saccharin consumption following administration of CCK and consuming the same saccharin solution following the drug vehicle. Once the discrimination was acquired, a generalization function was determined for doses above and below that of the training stimulus. At doses below the training dose of CCK (i.e., 0, 3.2, and 4.2 micrograms/kg), subjects drank at control levels, whereas at the training dose and above (10 micrograms/kg) subjects significantly reduced consumption. That a relatively low dose of CCK can be used as a discriminative stimulus within a drug discrimination design may be important in that the procedure can now be used in the assessment of the pharmacological characteristics of CCK at a dose similar to that used in other behavioral assessments of the compound.

Remarkable results have been obtained using image models based on image patches, for example sparse generative models for image inpainting, noise reduction and superresolution, sparse texture segmentation or texton models. In this paper we propose a powerful and yet simple approach for segmentation...... using dictionaries of image patches with associated label data. The approach is based on ideas from sparse generative image models and texton based texture modeling. The intensity and label dictionaries are learned from training images with associated label information of (a subset) of the pixels based...

This paper presents a novel solution to track a visual object under changes in illumination, viewpoint, pose, scale, and occlusion. Under the framework of sequential Bayesian learning, we first develop a discriminative model-based tracker with a fast relevance vector machine algorithm, and then, a generative model-based tracker with a novel sequential Gaussian mixture model algorithm. Finally, we present a three-level hierarchy to investigate different schemes to combine the discriminative and generative models for tracking. The presented hierarchical model combination contains the learner combination (at level one), classifier combination (at level two), and decision combination (at level three). The experimental results with quantitative comparisons performed on many realistic video sequences show that the proposed adaptive combination of discriminative and generative models achieves the best overall performance. Qualitative comparison with some state-of-the-art methods demonstrates the effectiveness and efficiency of our method in handling various challenges during tracking.

Tramadol is an unscheduled atypical analgesic that acts as an agonist at μ-opioid receptors and inhibits monoamine reuptake. Tramadol can suppress opioid withdrawal, and chronic administration can produce opioid physical dependence; however, diversion and abuse of tramadol is low. The present study further characterized tramadol in a three-choice discrimination procedure. Nondependent volunteers with active stimulant and opioid use (n = 8) participated in this residential laboratory study. Subjects were trained to discriminate between placebo, hydromorphone (8 mg), and methylphenidate (60 mg), and tests of acquisition confirmed that all volunteers could discriminate between the training drugs. The following drug conditions were then tested during discrimination test sessions: placebo, hydromorphone (4 and 8 mg), methylphenidate (30 and 60 mg), and tramadol (50, 100, 200, and 400 mg). In addition to discrimination measures, which included discrete choice, point distribution, and operant responding, subjective and physiological effects were measured for each test condition. Both doses of hydromorphone and methylphenidate were identified as hydromorphone- and methylphenidate-like, respectively. Lower doses of tramadol were generally identified as placebo, with higher doses (200 and 400 mg) identified as hydromorphone, or opioid-like. The highest dose of tramadol increased ratings on the stimulant scale, but was not significantly identified as methylphenidate-like. Tramadol did not significantly increase subjective ratings associated with reinforcement. Taken together, these results extend previous work with tramadol as a potential medication for the treatment of opioid dependence and withdrawal, showing acute doses of tramadol exhibit a profile of effects similar to opioid agonists and may have abuse liability in certain populations. PMID:21467190

Two methods investigating learning and memory in juvenile Göttingen minipigs were evaluated for potential use in preclinical toxicity testing. Twelve minipigs were tested using a spatial hole-board discrimination test including a learning phase and two memory phases. Five minipigs were tested in a visual discrimination test. The juvenile minipigs were able to learn the spatial hole-board discrimination test and showed improved working and reference memory during the learning phase. Performance in the memory phases was affected by the retention intervals, but the minipigs were able to remember the concept of the test in both memory phases. Working memory and reference memory were significantly improved in the last trials of the memory phases. In the visual discrimination test, the minipigs learned to discriminate between the three figures presented to them within 9-14 sessions. For the memory test, all minipigs performed 9/12 correct choices or better. Juvenile Göttingen minipigs are able to learn to perform in a spatial hole-board discrimination test as well as in a visual discrimination test, showing an increase in performance over time. Both tests have considerable scope to assess learning and memory of pigs, and we seem to have succeeded in establishing two test systems suitable for performing preclinical toxicity testing in juvenile minipigs.

than the commonly used feature concatenation, leading to a low complexity. Furthermore, there is no positive semi-definitive constrain on the transformation matrix while there is in metric learning methods, leading to an easy solution for the transformation matrix. Experimental results on two public......In this paper, we present a discriminative feature difference learning method for facial image based kinship verification. To transform feature difference of an image pair to be discriminative for kinship verification, a linear transformation matrix for feature difference between an image pair...... is inferred from training data. This transformation matrix is obtained through minimizing the difference of L2 norm between the feature difference of each kinship pair and its neighbors from non-kinship pairs. To find the neighbors, a cosine similarity is applied. Our method works on feature difference rather...

Automatic image annotation (AIA) raises tremendous challenges to machine learning as it requires modeling of data that are both ambiguous in input and output, e.g., images containing multiple objects and labeled with multiple semantic tags. Even more challenging is that the number of candidate tags is usually huge (as large as the vocabulary size) yet each image is only related to a few of them. This paper presents a hybrid generative-discriminative classifier to simultaneously address the extreme data-ambiguity and overfitting-vulnerability issues in tasks such as AIA. Particularly: (1) an Exponential-Multinomial Mixture (EMM) model is established to capture both the input and output ambiguity and in the meanwhile to encourage prediction sparsity; and (2) the prediction ability of the EMM model is explicitly maximized through discriminativelearning that integrates variational inference of graphical models and the pairwise formulation of ordinal regression. Experiments show that our approach achieves both su...

Full Text Available Abstract Background Since females often pay a higher cost for heterospecific matings, mate discrimination and species recognition are driven primarily by female choice. In contrast, frequent indiscriminate matings are hypothesized to maximize male fitness. However, recent studies show that previously indiscriminate males (e.g., Drosophila melanogaster and Poecilia reticulata can learn to avoid heterospecific courtship. This ability of males to discriminate against heterospecific courtship may be advantageous in populations where two species co-occur if courtship or mating is costly. Results Here, we tested whether Drosophila pseudoobscura males learn to discriminate against heterospecific females after being exposed to and rejected by D. persimilis females. In most of our assays, we failed to observe differences in D. pseudoobscura courtship intensity of heterospecific females by males that had previously courted heterospecific females vs. males that had been maintained in isolation. Conclusion We conclude that learning to avoid heterospecific courtship may not be universal, even within the genus Drosophila, and may possibly be dependent on the natural history of the species.

Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the l0 or l1 norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity term and the Laplacian term to characterize the intrinsic data structure. The lower level is subordinate to the upper level. Therefore, our model achieves an overall optimality for recognition in that the learnt dictionary is directly tailored for recognition. Moreover, the sparse codes learning models in the training and the testing phases can be the same. We further propose a novel method to solve our bilevel optimization problem. It first replaces the lower level with its Karush-Kuhn-Tucker conditions and then applies the alternating direction method of multipliers to solve the equivalent problem. Extensive experiments demonstrate the effectiveness and robustness of our method.

针对一般聚类获得的码本缺乏判别性表示导致不能有效进行人体动作识别的问题，提出了一种新的自适应码本学习方法，该方法将判别式词袋（bag of words，BoW）动作表示和自适应码本学习结合，增强了码本的表示能力和特征的判别性。为了有效求解非凸目标函数，提出基于轮换优化迭代方法，即固定码本更新判别矩阵，然后判别矩阵更新固定码本，直至满足终止迭代条件，该方法为自适应码本学习提供了技术支持。仿真实验采用KTH、Hollywood2、芭蕾、i3 Dpost数据库进行判别比较，识别率比现有典型方法平均提高了4％左右，学习到的码本在特征空间中具有良好的判别性能。相比于基于光流、方向梯度直方图（histograms of oriented gradients， HOG）等方法，计算复杂度更低，实用性更好。%As the problem of ineffective human action recognition caused by lack of judgment representation of codebook ob-tained by general clustering,this paper proposed a new adaptive codebook learning method,combined discriminant bag of words with adaptive codebook learning,which had enhanced the ability to represent features of codebook discriminant.In order to ef-fectively solve the non-convex objective function,it proposed iteration of rotational optimization,that was the fixed codebook up-dated the judgment matrix,and then the judgment matrix updated the fixed codebook until it met the conditions for termination of the iteration.This method provided technical support for the adaptive codebook learning.In the simulation experiments,it tested five data sets KTH,Hollywood2,ballet,i3Dpost and facial expressions.The experimental result is about 4% recognition rate averagely higher than the existing typical methods.The learned codebook has good discrimination performance in the fea-ture space.Compared with optical flow method,the method based on the histograms of oriented gradients (HOG

The activities collected in this handbook are planned for parents to use with their children in a learning experience. They can also be used in the classroom. Sections contain games designed to develop visual discrimination, auditory discrimination, motor coordination and oral expression. An objective is given for each game, and directions for…

Specific immunoglobulin (IgA, IgG and IgM) responses to different antigen targets (soluble eggs antigen--SEA, soluble worm adult protein--SWAP and keyhole limpet hole--KLH) were measured by enzyme linked immunosorbent assay (ELISA) in patients with acute and chronic schistosomiasis, as well as patients without schistosomiasis. SEA IgA and KLH IgM presented high discriminatory powers to distinguish acute from chronic schistosomiasis, with calculated areas under the curve (AUCs) of 0.88 and 0.82, respectively, obtained from receiver operating characteristic (ROC) curve. On the other hand, these tests, particularly SEA IgA were not useful to distinguish schistosomiasis (including the acute and chronic forms) from individuals without this disease, but infected with other intestinal parasites (Ascaris lumbricoides, Trichuris trichiura and hookworm). By contrast, SWAP IgG and SEA IgG were able to discriminate schistosomiasis patients from healthy individuals and patients infected with other parasites (AUCs of 0.96 and 0.85, respectively). Thus, it is possible to use a combination of serological tests, such as SEA IgA and SWAP IgG, to simultaneously establish the diagnosis of schistosomiasis and discriminate the acute from the chronic forms of the disease.

Full Text Available This paper investigates the concept of the human rights of women and its connection with the phenomenon and the instances of discrimination against women. Discrimination against women, its social visibility and the fight against it, within the idea of the rights and the equality of women, are a source of many theoretical debates. Academic discussions and a powerful influence of the women's movement have brought about the establishment and the exercise of the human rights of women at different levels of the public and the private spheres of society, as a substantial part of the universal regime of human rights.

Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminantlearning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function.

This innovative textbook is the first to integrate learning and memory, behaviour, and cognition. It focuses on fascinating human research in both memory and learning (while also bringing in important animal studies) and brings the reader up to date with the latest developments in the subject. Students are encouraged to think critically: key…

"Peak shift" is a behavioral response bias arising from discriminationlearning in which animals display a directional, but limited, preference for or avoidance of unusual stimuli. Its hypothesized evolutionary relevance has been primarily in the realm of aposematic coloration and limited sexual dimorphism. Here, we develop a novel functional approach to peak shift, based on signal detection theory, which characterizes the response bias as arising from uncertainty about stimulus appearance, frequency, and quality. This approach allows the influence of peak shift to be generalized to the evolution of signals in a variety of domains and sensory modalities. The approach is illustrated with a bumblebee (Bombus impatiens) discriminationlearning experiment. Bees exhibited peak shift while foraging in an artificial Batesian mimicry system. Changes in flower abundance, color distribution, and visitation reward induced bees to preferentially visit novel flower colors that reduced the risk of flower-type misidentification. Under conditions of signal uncertainty, peak shift results in visitation to rarer, but more easily distinguished, morphological variants of rewarding species in preference to their average morphology. Peak shift is a common and taxonomically widespread phenomenon. This example of the possible role of peak shift in signal evolution can be generalized to other systems in which a signal receiver learns to make choices in situations in which signal variation is linked to the sender's reproductive success.

Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. Acquisition of large training sets is one of the key challenges, when approaching a new task. In this paper, we aim for generic feature learning and present an approach for training a convolutional network using only unlabeled data. To this end, we train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. In contrast to supervised network training, the resulting feature representation is not class specific. It rather provides robustness to the transformations that have been applied during training. This generic feature representation allows for classification results that outperform the state of the art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101, Caltech-256). While features learned with our approach cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor.

Stein kernel (SK) has recently shown promising performance on classifying images represented by symmetric positive definite (SPD) matrices. It evaluates the similarity between two SPD matrices through their eigenvalues. In this paper, we argue that directly using the original eigenvalues may be problematic because: 1) eigenvalue estimation becomes biased when the number of samples is inadequate, which may lead to unreliable kernel evaluation, and 2) more importantly, eigenvalues reflect only the property of an individual SPD matrix. They are not necessarily optimal for computing SK when the goal is to discriminate different classes of SPD matrices. To address the two issues, we propose a discriminative SK (DSK), in which an extra parameter vector is defined to adjust the eigenvalues of input SPD matrices. The optimal parameter values are sought by optimizing a proxy of classification performance. To show the generality of the proposed method, three kernel learning criteria that are commonly used in the literature are employed as a proxy. A comprehensive experimental study is conducted on a variety of image classification tasks to compare the proposed DSK with the original SK and other methods for evaluating the similarity between SPD matrices. The results demonstrate that the DSK can attain greater discrimination and better align with classification tasks by altering the eigenvalues. This makes it produce higher classification performance than the original SK and other commonly used methods.

Full Text Available Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminativelearning to maximize annotation accuracy. Among discriminativelearning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.

In the field of image classification, it has been a trend that in order to deliver a reliable classification performance, the feature extraction model becomes increasingly more complicated, leading to a high dimensionality of image representations. This, in turn, demands greater computation resources for image classification. Thus, it is desirable to apply dimensionality reduction (DR) methods for image classification. It is necessary to apply DR methods to relieve the computational burden as well as to improve the classification accuracy. However, traditional DR methods are not compatible with modern feature extraction methods. A framework that combines manifold learning based DR and feature extraction in a deeper way for image classification is proposed. A multiscale cell representation is extracted from the spatial pyramid to satisfy the locality constraints for a manifold learning method. A spectral weighted mean filtering is proposed to eliminate noise in the feature space. A hierarchical discriminant manifold learning is proposed which incorporates both category label and image scale information to guide the DR process. Finally, the image representation is generated by concatenating dimensionality reduced cell representations from the same image. Extensive experiments are conducted to test the proposed algorithm on both scene and object recognition datasets in comparison with several well-established and state-of-the-art methods with respect to classification precision and computational time. The results verify the effectiveness of incorporating manifold learning in the feature extraction procedure and imply that the multiscale cell representations may be distributed on a manifold.

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available.

Using an automated learning device, we investigated "learning to learn" by dwarf goats (Capra hircus) in what was for them a familiar environment and normal social settings. Nine problems, each consisting of four discriminable black symbols, each with one S-super+ and three different S-super(-), were presented on a computer screen. Mean daily learning success improved over the course of the first four problems, and the improvement was maintained throughout the remaining five problems. The number of trials to reach the learning criterion decreased significantly beginning with problem four. Such results may be interpreted as evidence that the goats were developing a learning set. In the present case, the learning set appeared to have two components. One involved gaining familiarity and apparent understanding of the learning device and the basic requirements of the discrimination task. The second component involved learning potential error factors to be ignored, as well as learning commonalities that carried over from one problem to the next. Among the error factors, evidence of apparent preferences for specific symbols was seen, which had a predictable effect on performances.

To describe a political mapping on discrimination and homophobia associated to human immunodeficiency virus (HIV) in the context of public institutions in Mexico. The political mapping was conducted in six Mexican states. Stakeholders who were involved in HIV actions from public and private sectors were included. Semistructured interviews were applied to explore homophobia and discrimination associated with HIV. Information was systematized using the Policy Maker software, which is a good support for analyzing health policies. Discriminatory and homophobic practices in the public domain occurred, damaging people´s integrity via insults, derision and hate crimes. Most stakeholders expressed a supportive position to prevent discrimination and homophobia and some of them had great influence on policy-making decisions. It was found that state policy frameworks are less specific in addressing these issues. Homophobia and discrimination associated to HIV are still considered problematic in Mexico. Homophobia is a very sensitive issue that requires further attention. Also, an actual execution of governmental authority requires greater enforcement of laws against discrimination and homophobia.

Gender discrimination has contributed to the gender imbalance in scientific fields. However, research on the effects of informing adolescent girls about gender discrimination in these fields is rare and controversial. To examine the consequences of learning about gender-based occupational discrimination, adolescent girls (n= 158, ages 11 to 14)…

Existing block-diagonal representation studies mainly focuses on casting block-diagonal regularization on training data, while only little attention is dedicated to concurrently learning both block-diagonal representations of training and test data. In this paper, we propose a discriminative block-diagonal low-rank representation (BDLRR) method for recognition. In particular, the elaborate BDLRR is formulated as a joint optimization problem of shrinking the unfavorable representation from off-block-diagonal elements and strengthening the compact block-diagonal representation under the semisupervised framework of LRR. To this end, we first impose penalty constraints on the negative representation to eliminate the correlation between different classes such that the incoherence criterion of the extra-class representation is boosted. Moreover, a constructed subspace model is developed to enhance the self-expressive power of training samples and further build the representation bridge between the training and test samples, such that the coherence of the learned intraclass representation is consistently heightened. Finally, the resulting optimization problem is solved elegantly by employing an alternative optimization strategy, and a simple recognition algorithm on the learned representation is utilized for final prediction. Extensive experimental results demonstrate that the proposed method achieves superb recognition results on four face image data sets, three character data sets, and the 15 scene multicategories data set. It not only shows superior potential on image recognition but also outperforms the state-of-the-art methods.

Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for ICA-based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis.Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real world EEG data set comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study.

Full Text Available Abstract Background To assess whether a compound is druglike or not as early as possible is always critical in drug discovery process. There have been many efforts made to create sets of 'rules' or 'filters' which, it is hoped, will help chemists to identify 'drug-like' molecules from 'non-drug' molecules. However, among the chemical space of the druglike molecules, the minority will be approved drugs. Classifying approved drugs from experimental drugs may be more helpful to obtain future approved drugs. Therefore, discrimination of approved drugs from experimental ones has been done in this paper by analyzing the compounds in terms of existing drugs features and machine learning methods. Results Four methodologies were compared by their performance to classify approved drugs from experimental ones. The best results were obtained by SVM, in which the accuracy is 0.7911, the sensitivity is 0.5929, and the specificity is 0.8743. Based on the results, consensus model was developed to effectively discriminate drugs, which further pushed the correct classification rate up to 0.8517, sensitivity up to 0.7242, specificity up to 0.9352. The applications on the Traditional Chinese Medicine Ingredients Database (TCM-ID tested the methods. Therefore this model has been proven to be a potent tool for identifying drug molecules. Conclusion The studies would have potential applications in the research of combinatorial library design and virtual high throughput screening for drug discovery.

This article presents "Learning to be Human", which John Macmurray delivered on 5 May 1958 as the annual public lecture at Moray House College of Education, now part of Edinburgh University. The key themes of the paper are ones to which Macmurray returned again and again in both his educational and his philosophical writing for over 40 years and…

Two experiments were conducted with the aim of designing a videogame for the study of human conditioned avoidance. Participants had to destroy enemy spaceships with the goal of increasing the score in a counter. Coloured signals might announce the launching of a bomb that could hit participant's spaceship producing a 30 points decrease in participant's score. Three groups of participants were trained in discriminating between a warning signal (W) and a safety signal (S) in Experiment 1. Instrumental group could avoid the loss of points by hiding the spaceship before the offset of W. Participants in the Yoked group received the same treatment received by their instrumental partners, regardless of their behaviour. In the Pavlovian group, W was always followed by the loss of points, regardless of participant's behaviour. Discrimination between W and S was better in the Instrumental groups than in the Yoked and Pavlovian control groups. Experiment 2 found extinction of avoidance when the warning signal was not followed by the bomb. Temporal discrimination was found within the participants that received the instrumental contingency in both experiments, with higher avoidance response towards the end of the warning signal. Temporal discrimination disappeared after extinction in Experiment 2.

... of discrimination in the labour market as well as to the mechanisms involved in discriminatory hiring practices. The design has several advantages compared to -‘single-method’ approaches and provides a more substantial understanding of the processes leading to ethnic inequality in the labour market.

The purpose of this experiment was to compare components of the human and rat auditory event-related potential (ERP) in a serial feature-positive discrimination task. Subjects learned to respond to an auditory target stimulus when it followed a visual feature (X→A+), but to not respond when it was

Recognition of environmental sounds is believed to proceed through discrimination steps from broad to more narrow categories. Very little is known about the neural processes that underlie fine-grained discrimination within narrow categories or about their plasticity in relation to newly acquired expertise. We investigated how the cortical representation of birdsongs is modulated by brief training to recognize individual species. During a 60-minute session, participants learned to recognize a set of birdsongs; they improved significantly their performance for trained (T) but not control species (C), which were counterbalanced across participants. Auditory evoked potentials (AEPs) were recorded during pre- and post-training sessions. Pre vs. post changes in AEPs were significantly different between T and C i) at 206-232ms post stimulus onset within a cluster on the anterior part of the left superior temporal gyrus; ii) at 246-291ms in the left middle frontal gyrus; and iii) 512-545ms in the left middle temporal gyrus as well as bilaterally in the cingulate cortex. All effects were driven by weaker activity for T than C species. Thus, expertise in discriminating T species modulated early stages of semantic processing, during and immediately after the time window that sustains the discrimination between human vs. animal vocalizations. Moreover, the training-induced plasticity is reflected by the sharpening of a left lateralized semantic network, including the anterior part of the temporal convexity and the frontal cortex. Training to identify birdsongs influenced, however, also the processing of C species, but at a much later stage. Correct discrimination of untrained sounds seems to require an additional step which results from lower-level features analysis such as apperception. We therefore suggest that the access to objects within an auditory semantic category is different and depends on subject's level of expertise. More specifically, correct intra

Full Text Available Since Greenwood and Woods' (1919 study in tendency of accident, many researchers have insisted that various human factors (sensation seeking, anger, anxiety are highly correlated with reckless driving and traffic accidents. Oh and Lee (2011 designed the Driving Behavior Determinants Questionnaire, a psychological tool to predict danger level of drivers and discriminate them into three groups (normal, unintentionally reckless, and intentionally reckless by their characteristics, attitude, and expected reckless behavior level. This tool's overall accuracy of discrimination was 70%. This study aimed to prove that the discrimination reflects the behavioral difference of drivers. Twenty-four young drivers were requested to react to the visual stimuli (tests for subjective speed sense, simple visual reaction time, and left turning at own risk. The results showed no differences in subjective speed sense among the driver groups, which means drivers' excessive speeding behaviors occur due to intention based on personality and attitude, not because of sensory disorders. In addition, there were no differences in simple reaction time among driver groups. However, the results of the ‘Left turning at drivers’ own risk task” revealed significant group differences. All reckless drivers showed a greater degree of dangerous left turning behaviors than the normal group did.

The lateral occipital cortex (LOC) activates selectively to images of intact objects versus scrambled controls, is selective for the figure-ground relationship of a scene, and exhibits at least some degree of invariance for size and position. Because of these attributes, it is considered to be a crucial part of the object recognition pathway. Here we show that human LOC is critically involved in perceptual decisions about object shape. High-density EEG was recorded while subjects performed a threshold-level shape discrimination task on texture-defined figures segmented by either phase or orientation cues. The appearance or disappearance of a figure region from a uniform background generated robust visual evoked potentials throughout retinotopic cortex as determined by inverse modeling of the scalp voltage distribution. Contrasting responses from trials containing shape changes that were correctly detected (hits) with trials in which no change occurred (correct rejects) revealed stimulus-locked, target-selective activity in the occipital visual areas LOC and V4 preceding the subject's response. Activity that was locked to the subjects' reaction time was present in the LOC. Response-locked activity in the LOC was determined to be related to shape discrimination for several reasons: shape-selective responses were silenced when subjects viewed identical stimuli but their attention was directed away from the shapes to a demanding letter discrimination task; shape-selectivity was present across four different stimulus configurations used to define the figure; LOC responses correlated with participants' reaction times. These results indicate that decision-related activity is present in the LOC when subjects are engaged in threshold-level shape discriminations.

Full Text Available The present report describes an animal model for examining the effects of radiation on a range of neurocognitive functions in rodents that are similar to a number of basic human cognitive functions. Fourteen male Long-Evans rats were trained to perform an automated intra-dimensional set shifting task that consisted of their learning a basic discrimination between two stimulus shapes followed by more complex discrimination stages (e.g., a discrimination reversal, a compound discrimination, a compound reversal, a new shape discrimination, and an intra-dimensional stimulus discrimination reversal. One group of rats was exposed to head-only X-ray radiation (2.3 Gy at a dose rate of 1.9 Gy/min, while a second group received a sham-radiation exposure using the same anesthesia protocol. The irradiated group responded less, had elevated numbers of omitted trials, increased errors, and greater response latencies compared to the sham-irradiated control group. Additionally, social odor recognition memory was tested after radiation exposure by assessing the degree to which rats explored wooden beads impregnated with either their own odors or with the odors of novel, unfamiliar rats; however, no significant effects of radiation on social odor recognition memory were observed. These data suggest that rodent tasks assessing higher-level human cognitive domains are useful in examining the effects of radiation on the CNS, and may be applicable in approximating CNS risks from radiation exposure in clinical populations receiving whole brain irradiation.

The current work investigated the neural correlates of visual perceptual learning in grating orientation discrimination by recording event-related potentials (ERPs) from human adults. Subjects were trained with a discrimination task of grating orientation in three consecutive training sessions within 2 h. While reaction times (RTs) were shortened gradually across training sessions, the N1 was decreased and the P2 was increased over the parietal and occipital areas. A broadly distributed P3 was increased along with more practices. In addition, the time course of learning reflected in the P2 and P3 amplitudes was in line with the changes of reaction times and exhibited a stable level during later training. The implications of these results to the neural mechanisms subserving perceptual learning were discussed.

In goal-directed pursuits, the basolateral amygdala (BLA) is critical in learning about changes in the value of rewards. BLA-lesioned rats show enhanced reversal learning, a task employed to measure the flexibility of response to changes in reward. Similarly, there is a trend for enhanced discriminationlearning, suggesting that BLA may modulate formation of stimulus-reward associations. There is a parallel literature on the importance of serotonin (5HT) in new stimulus-reward and reversal learning. Recent postulations implicate 5HT in learning from punishment. Whereas, dopaminergic involvement is critical in behavioral activation and reinforcement, 5HT may be most critical for aversive processing and behavioral inhibition, complementary cognitive processes. Given these findings, a 5HT-mediated mechanism in BLA may mediate the facilitated learning observed previously. The present study investigated the effects of selective 5HT lesions in BLA using 5,7-dihydroxytryptamine (5,7-DHT) vs. infusions of saline (Sham) on discrimination, retention, and deterministic reversal learning. Rats were required to reach an 85% correct pairwise discrimination and single reversal criterion prior to surgery. Postoperatively, rats were then tested on the (1) retention of the pretreatment discrimination pair, (2) discrimination of a novel pair, and (3) reversal learning performance. We found statistically comparable preoperative learning rates between groups, intact postoperative retention, and unaltered novel discrimination and reversal learning in 5,7-DHT rats. These findings suggest that 5HT in BLA is not required for formation and flexible adjustment of new stimulus-reward associations when the strategy to efficiently solve the task has already been learned. Given the complementary role of orbitofrontal cortex in reward learning and its interconnectivity with BLA, these findings add to the list of dissociable mechanisms for BLA and orbitofrontal cortex in reward learning.

Full Text Available In goal-directed pursuits, the basolateral amygdala (BLA is critical in learning about changes in the value of rewards. BLA-lesioned rats show enhanced reversal learning, a task employed to measure the flexibility of response to changes in reward. Similarly, there is a trend for enhanced discriminationlearning, suggesting that BLA may modulate formation of stimulus-reward associations. There is a parallel literature on the importance of serotonin (5HT in new stimulus-reward and reversal learning. Recent postulations implicate 5HT in learning from punishment. Whereas dopaminergic involvement is critical in behavioral activation and reinforcement, 5HT may be most critical for aversive processing and behavioral inhibition, complementary cognitive processes. Given these findings, a 5HT-mediated mechanism in BLA may mediate the facilitated learning observed previously. The present study investigated the effects of selective 5HT lesions in BLA using 5,7-dihydroxytryptamine (5,7-DHT versus infusions of saline (Sham on discrimination, retention, and deterministic reversal learning. Rats were required to reach an 85% correct pairwise discrimination and single reversal criterion prior to surgery. Postoperatively, rats were then tested on the 1 retention of the pretreatment discrimination pair 2 discrimination of a novel pair and 3 reversal learning performance. We found statistically comparable preoperative learning rates between groups, intact postoperative retention, and unaltered novel discrimination and reversal learning in 5,7-DHT rats. These findings suggest that 5HT in BLA is not required for formation and flexible adjustment of new stimulus-reward associations when the strategy to efficiently solve the task has already been learned. Given the complementary role of orbitofrontal cortex in reward learning and its interconnectivity with BLA, these findings add to the list of dissociable mechanisms for BLA and orbitofrontal cortex in reward learning.

To study laser-induced autofluorescence spectroscopy of the human lung cell line, we evaluated the native fluorescence properties of cancer QU-DB and normal MRC-5 human lung cells during continuous exposure to 405 nm laser light. Two emission bands centered at ~470 nm and ~560 nm were observed. These peaks are most likely attributable to mitochondrial fluorescent reduced nicotinamide adenine dinucleotide and riboflavin fluorophores, respectively. This article highlights lung cell autofluorescence characterization and signal discrimination by collective investigation of different spectral features. The absolute intensity, the spectral shape factor or redox ratio, the full width of half-maximum and the full width of quarter maximum was evaluated. Moreover, the intensity ratio, the area under the peak and the area ratio as a contrast factor for normal and cancerous cells were also calculated. Among all these features it seems that the contrast factor precisely and significantly discriminates the spectral differences of normal and cancerous lung cells. On the other hand, the relative quantum yield for both cell types were found by comparing the quantum yield of an unknown compound with known fluorescein sodium as a reference solution.

In this paper, we will examine key points for research attention in the effort to commit educational systems to equity education. We will examine the concepts of equity, equality and discrimination. We will give specific attention to the role of teacher educators. Teachers need to understand and to be able to see social discrimination in…

This paper describes an optimal mapping of the torus self-organizing map for a human forearm motion discrimination on the basis of the myoelectric signals. This study uses the torus self-organizing map (Torus-SOM) for the motion discrimination. The normal SOM identify input data into the same feature group by using the all units of map. Then there is a possibility of the misrecognition motion around the boundary lines of the motion groups. Therefore, this study proposes the mapping method of SOM that the learning units of the same motion concentrate on one local range and the learning unit groups of each motion separates enough. As a result, the variance in the same motion group becomes small and the variance between each motion groups becomes big. Some experiments on the myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.

. One of the key issues when designing such training systems is in the assessment of transfer of learning. In this study we present data on the learning of an auditory task involving sinusoidal amplitude- and frequency-modulated tones. Modulation rate discrimination thresholds were measured during pre......-training, training, a post-training stages. During training, listeners were divided into two groups; one group trained on amplitude-modulation rate discrimination and the other group trained on frequency-modulation rate discrimination. Results will be discussed in terms of their implications for training...

The applicability areas for sensor networks vary from industrial automation, environmental observation to medical domain [1]. As the quality of life has improved, the life expectancy also increased during the last years, fact that leads to an aging of the population. It is well known that elderly people need special treatment and resources due to their decreasing capacity of self-caring. It is, thus, desirable to increase the length of independent living for this category without depriving them from the known life environment and personal habits. Another possible application is the one of child care and monitoring in closed precincts. This paper illustrates the implementation steps of a sensor network used for discriminating between the presence of a human being and of an animal that may be useful in case of medical emergency situations. The design takes into account the main challenges that may occur such as achievement of not accurate results due to the fact that children are moving much more than an adult. The basic structure is designed using Arduino platform, sensors for distance measurements, for height determination as well as DHT22 temperature sensor and sensors for motion detection and takes into account cases of walking and standing subjects. Several configurations have been tested in order to improve the relative error for discrimination between children and pet entering a room.

Recent theories posit that adult neurogenesis supports dentate gyrus pattern separation and hence is necessary for some types of discriminationlearning. Using an inducible transgenic mouse model, we investigated the contribution of adult-born neurons to spatial and nonspatial touch-screen discriminations of varying levels of difficulty. Arresting neurogenesis caused a modest but statistically significant impairment in a position discrimination task. However, the effect was present only on trials after a learneddiscrimination was reversed, suggesting that neurogenesis supports cognitive flexibility rather than spatial discrimination per se. The deficit was present 4-10 weeks after the arrest of neurogenesis but not immediately after, consistent with previous evidence that the behavioral effects of arresting neurogenesis arise because of the depletion of adult-born neurons at least 1 month old. The arrest of neurogenesis failed to affect a nonspatial brightness discrimination task that was equal in difficulty to the spatial task. The data suggest that adult neurogenesis is not strictly necessary for spatial or perceptual discriminationlearning and instead implicate adult neurogenesis in factors related to reversal learning, such as cognitive flexibility or proactive interference.

The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann machines may outperform these standard filter banks because they learn a feature description directly from the training data. Like many other representation learning methods, restricted Boltzmann machines are unsupervised and are trained with a generative learning objective; this allows them to learn representations from unlabeled data, but does not necessarily produce features that are optimal for classification. In this paper we propose the convolutional classification restricted Boltzmann machine, which combines a generative and a discriminativelearning objective. This allows it to learn filters that are good both for describing the training data and for classification. We present experiments with feature learning for lung texture classification and airway detection in CT images. In both applications, a combination of learning objectives outperformed purely discriminative or generative learning, increasing, for instance, the lung tissue classification accuracy by 1 to 8 percentage points. This shows that discriminativelearning can help an otherwise unsupervised feature learner to learn filters that are optimized for classification.

There have been several notable recent trends in the area of learning and memory. Problems with the episodic/semantic distinction have become more apparent, and new efforts have been made (exemplar models, distributed-memory models) to represent general knowledge without assuming a separate semantic system. Less emphasis is being placed on stable, prestored prototypes and more emphasis on a flexible memory system that provides the basis for a multitude of categories or frames of reference, derived on the spot as tasks demand. There is increasing acceptance of the idea that mental models are constructed and stored in memory in addition to, rather than instead of, memorial representations that are more closely tied to perceptions. This gives rise to questions concerning the conditions that permit inferences to be drawn and mental models to be constructed, and to questions concerning the similarities and differences in the nature of the representations in memory of perceived and generated information and in their functions. There has also been a swing from interest in deliberate strategies to interest in automatic, unconscious (even mechanistic!) processes, reflecting an appreciation that certain situations (e.g. recognition, frequency judgements, savings in indirect tasks, aspects of skill acquisition, etc) seem not to depend much on the products of strategic, effortful or reflective processes. There is a lively interest in relations among memory measures and attempts to characterize memory representations and/or processes that could give rise to dissociations among measures. Whether the pattern of results reflects the operation of functional subsystems of memory and, if so, what the "modules" are is far from clear. This issue has been fueled by work with amnesics and has contributed to a revival of interaction between researchers studying learning and memory in humans and those studying learning and memory in animals. Thus, neuroscience rivals computer science as a

Associative learning plays an important role in the development of anxiety disorders, but a thorough understanding of the variables that impact such learning is still lacking. We investigated whether individual differences in autobiographical memory specificity are related to discriminationlearning and generalization. In an associative learning task, participants learned the association between two pictures of female faces and a non-aversive outcome. Subsequently, six morphed pictures functioning as generalization stimuli (GSs) were introduced. In a sample of healthy participants (Study 1), we did not find evidence for differences in discriminationlearning as a function of memory specificity. In a sample of anxiety disorder patients (Study 2), individuals who were characterized by low memory specificity showed deficient discriminationlearning relative to high specific individuals. In contrast to previous findings, results revealed no effect of memory specificity on generalization. These results indicate that impaired discriminationlearning, previously shown in patients suffering from an anxiety disorder, may be—in part—due to limited memory specificity. Together, these studies emphasize the importance of incorporating cognitive variables in associative learning theories and their implications for the development of anxiety disorders. In addition, re-analyses of the data (Study 3) showed that patients suffering from panic disorder showed higher outcome expectancies in the presence of the stimulus that was never followed by an outcome during discrimination training, relative to patients suffering from other anxiety disorders and healthy participants. Because we used a neutral, non-aversive outcome (i.e., drawing of a lightning bolt), these data suggest that learning abnormalities in panic disorder may not be restricted to fear learning, but rather reflect a more general associative learning deficit that also manifests in fear irrelevant contexts. PMID

The type of stimulus material employed in visual tasks is crucial to all comparative cognition research that involves object recognition. There is considerable controversy about the use of 2-dimensional stimuli and the impact that the lack of the 3rd dimension (i.e., depth) may have on animals' performance in tests for their visual and cognitive abilities. We report evidence of discriminationlearning using a completely novel type of stimuli, namely, holograms. Like real objects, holograms provide full 3-dimensional shape information but they also offer many possibilities for systematically modifying the appearance of a stimulus. Hence, they provide a promising means for investigating visual perception and cognition of different species in a comparative way. We trained pigeons and humans to discriminate either between 2 real objects or between holograms of the same 2 objects, and we subsequently tested both species for the transfer of discrimination to the other presentation mode. The lack of any decrements in accuracy suggests that real objects and holograms were perceived as equivalent in both species and shows the general appropriateness of holograms as stimuli in visual tasks. A follow-up experiment involving the presentation of novel views of the training objects and holograms revealed some interspecies differences in rotational invariance, thereby confirming and extending the results of previous studies. Taken together, these results suggest that holograms may not only provide a promising tool for investigating yet unexplored issues, but their use may also lead to novel insights into some crucial aspects of comparative visual perception and categorization.

We analyze statistical features of background and clustered subpopulations of earthquakes in different regions in an effort to distinguish between human-induced and natural seismicity. Analysis of "end-member" areas known to be dominated by human-induced earthquakes (the Geyser geothermal field in northern California and TauTona gold mine in South Africa) and regular tectonic activity (the San Jacinto fault zone in southern California and Coso region excluding the Coso geothermal field in eastern central California) reveals several distinguishing characteristics. Induced seismicity is shown to have (i) higher rate of background events (both absolute and relative to the total rate), (ii) faster temporal offspring decay, (iii) higher intensity of repeating events, (iv) larger proportion of small clusters, and (v) larger spatial separation between parent and offspring, compared to regular tectonic activity. These differences also successfully discriminate seismicity within the Coso and Salton Sea geothermal fields in California before and after the expansion of geothermal production during the 1980s.

Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn f

In goal-directed pursuits, the basolateral amygdala (BLA) is critical in learning about changes in the value of rewards. BLA-lesioned rats show enhanced reversal learning, a task employed to measure the flexibility of response to changes in reward. Similarly, there is a trend for enhanced discriminationlearning, suggesting that BLA may modulate formation of stimulus-reward associations. There is a parallel literature on the importance of serotonin (5HT) in new stimulus-reward and reversal le...

Cortisol, the primary glucocorticoid (GC) in humans, influences neuronal excitability and plasticity by acting on mineralocorticoid and glucocorticoid receptors. Cellular studies demonstrated that elevated GC levels affect neuronal plasticity, for example through a reduction of hippocampal long-term potentiation (LTP). At the behavioural level, after treatment with GCs, numerous studies have reported impaired hippocampal function, such as impaired memory retrieval. In contrast, relatively little is known about the impact of GCs on cortical plasticity and perceptual learning in adult humans. Therefore, in this study, we explored the impact of elevated GC levels on human perceptual learning. To this aim, we used a training-independent learning approach, where lasting changes in human perception can be induced by applying passive repetitive sensory stimulation (rss), the timing of which was determined from cellular LTP studies. In our placebo-controlled double-blind study, we used tactile LTP-like stimulation to induce improvements in tactile acuity (spatial two-point discrimination). Our results show that a single administration of hydrocortisone (30mg) completely blocked rss-induced changes in two-point discrimination. In contrast, the placebo group showed the expected rss-induced increase in two-point discrimination of over 14%. Our data demonstrate that high GC levels inhibit rss-induced perceptual learning. We suggest that the suppression of LTP, as previously reported in cellular studies, may explain the perceptual learning impairments observed here.

A learning scheme based on Random Forests is used to discriminate the task to be executed using only myoelectric activity from the upper limb. Three different task features can be discriminated: subspace to move towards, object to be grasped and task to be executed (with the object). The discrimination between the different reach to grasp movements is accomplished with a random forests classifier, which is able to perform efficient features selection, helping us to reduce the number of EMG channels required for task discrimination. The proposed scheme can take advantage of both a classifier and a regressor that cooperate advantageously to split the task space, providing better estimation accuracy with task-specific EMG-based motion decoding models, as reported in [1] and [2]. The whole learning scheme can be used by a series of EMG-based interfaces, that can be found in rehabilitation cases and neural prostheses.

This article examines why organizations struggle with learning how to prevent discrimination against their employees with disabilities. To explore this issue, qualitative archival data were collected and analyzed from 53 Americans with Disabilities Act (ADA) lawsuits filed against 44 organizations. Theoretical analysis of the qualitative data suggests that several organizationally based learning theories explain the difficulty organizations have with creating a disability-friendly work environment. These barriers to learning are embedded in complex defense mechanisms and discriminatory organizational routines. Furthermore, organizations have difficulties engaging in higher-order and vicarious learning. We conclude the article with examples of successful learning practices as they relate to barriers identified in the qualitative analysis.

Full Text Available Recent applications and developments based on support vector machines (SVMs have shown that using multiple kernels instead of a single one can enhance classifier performance. However, there are few reports on performance of the kernel‐based Fisher discriminant analysis (kernel‐based FDA method with multiple kernels. This paper proposes a multiple kernel construction method for kernel‐based FDA. The constructed kernel is a linear combination of several base kernels with a constraint on their weights. By maximizing the margin maximization criterion (MMC, we present an iterative scheme for weight optimization. The experiments on the FERET and CMU PIE face databases show that, our multiple kernel Fisher discriminant analysis (MKFD achieves high recognition performance, compared with single‐kernel‐based FDA. The experiments also show that the constructed kernel relaxes parameter selection for kernel‐based FDA to some extent.

Despite the importance of action identification and discrimination in action perception and social cognition more broadly, little research has investigated how these processes are achieved. To this end, we sought to identify the extent to which adults capitalize on featural versus configural sources of information when discriminating small-scale…

Despite the importance of action identification and discrimination in action perception and social cognition more broadly, little research has investigated how these processes are achieved. To this end, we sought to identify the extent to which adults capitalize on featural versus configural sources of information when discriminating small-scale…

Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite its success in modeling the probability distribution of variables, BN is naturally a generative model, which is not necessarily discriminative. This may cause the ignorance of subtle but critical network changes that are of investigation values across populations. In this paper, we propose to improve the discriminative power of BN models for continuous variables from two different perspectives. This brings two general discriminativelearning frameworks for Gaussian Bayesian networks (GBN). In the first framework, we employ Fisher kernel to bridge the generative models of GBN and the discriminative classifiers of SVMs, and convert the GBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. In the second framework, we employ the max-margin criterion and build it directly upon GBN models to explicitly optimize the classification performance of the GBNs. The advantages and disadvantages of the two frameworks are discussed and experimentally compared. Both of them demonstrate strong power in learningdiscriminative parameters of GBNs for neuroimaging based brain network analysis, as well as maintaining reasonable representation capacity. The contributions of this paper also include a new Directed Acyclic Graph (DAG) constraint with theoretical guarantee to ensure the graph validity of GBN.

Humanlearning and memory depend on multiple cognitive systems related to dissociable brain structures. These systems interact not only in cooperative but also sometimes competitive ways in optimizing performance. Previous studies showed that manipulations reducing the engagement of frontal lobe-mediated explicit attentional processes could lead to improved performance in striatum-related procedural learning. In our study, hypnosis was used as a tool to reduce the competition between these 2 systems. We compared learning in hypnosis and in the alert state and found that hypnosis boosted striatum-dependent sequence learning. Since frontal lobe-dependent processes are primarily affected by hypnosis, this finding could be attributed to the disruption of the explicit attentional processes. Our result sheds light not only on the competitive nature of brain systems in cognitive processes but also could have important implications for training and rehabilitation programs, especially for developing new methods to improve humanlearning and memory performance.

Full Text Available The ability to discriminate tones of different frequencies is fundamentally important for everyday hearing. While neurons in the primary auditory cortex (AC respond differentially to tones of different frequencies, whether and how AC regulates auditory behaviors that rely on frequency discrimination remains poorly understood. Here, we find that the level of activity of inhibitory neurons in AC controls frequency specificity in innate and learned auditory behaviors that rely on frequency discrimination. Photoactivation of parvalbumin-positive interneurons (PVs improved the ability of the mouse to detect a shift in tone frequency, whereas photosuppression of PVs impaired the performance. Furthermore, photosuppression of PVs during discriminative auditory fear conditioning increased generalization of conditioned response across tone frequencies, whereas PV photoactivation preserved normal specificity of learning. The observed changes in behavioral performance were correlated with bidirectional changes in the magnitude of tone-evoked responses, consistent with predictions of a model of a coupled excitatory-inhibitory cortical network. Direct photoactivation of excitatory neurons, which did not change tone-evoked response magnitude, did not affect behavioral performance in either task. Our results identify a new function for inhibition in the auditory cortex, demonstrating that it can improve or impair acuity of innate and learned auditory behaviors that rely on frequency discrimination.

We have used a genetically tractable model system, the fruit fly Drosophila melanogaster to study the interdependence between sensory processing and associative processing on learning performance. We investigated the influence of variations in the physical and predictive properties of color stimuli in several different operant-conditioning procedures on the subsequent learning performance. These procedures included context and stimulus generalization as well as color, compound, and conditional discrimination (colors and patterns). A surprisingly complex dependence of the learning performance on the colors' physical and predictive properties emerged, which was clarified by taking into account the fly-subjective perception of the color stimuli. Based on estimates of the stimuli's color and brightness values, we propose that the different tasks are supported by different parameters of the color stimuli; generalization occurs only if the chromaticity is sufficiently similar, whereas discriminationlearning relies on brightness differences.

Full Text Available Discrimination of and memory for others' generous and selfish behaviors could be adaptive abilities in social animals. Dogs have seemingly expressed such skills in both direct and indirect interactions with humans. However, recent studies suggest that their capacity may rely on cues other than people's individual characteristics, such as the place where the person stands. Thus, the conditions under which dogs recognize individual humans when solving cooperative tasks still remains unclear. With the aim of contributing to this problem, we made dogs interact with two human experimenters, one generous (pointed towards the food, gave ostensive cues, and allowed the dog to eat it and the other selfish (pointed towards the food, but ate it before the dog could have it. Then subjects could choose between them (studies 1-3. In study 1, dogs took several training trials to learn the discrimination between the generous and the selfish experimenters when both were of the same gender. In study 2, the discrimination was learned faster when the experimenters were of different gender as evidenced both by dogs' latencies to approach the bowl in training trials as well as by their choices in preference tests. Nevertheless, dogs did not get confused by gender when the experimenters were changed in between the training and the choice phase in study 3. We conclude that dogs spontaneously used human gender as a cue to discriminate between more and less cooperative experimenters. They also relied on some other personal feature which let them avoid being confused by gender when demonstrators were changed. We discuss these results in terms of dogs' ability to recognize individuals and the potential advantage of this skill for their lives in human environments.

Discrimination of and memory for others' generous and selfish behaviors could be adaptive abilities in social animals. Dogs have seemingly expressed such skills in both direct and indirect interactions with humans. However, recent studies suggest that their capacity may rely on cues other than people's individual characteristics, such as the place where the person stands. Thus, the conditions under which dogs recognize individual humans when solving cooperative tasks still remains unclear. With the aim of contributing to this problem, we made dogs interact with two human experimenters, one generous (pointed towards the food, gave ostensive cues, and allowed the dog to eat it) and the other selfish (pointed towards the food, but ate it before the dog could have it). Then subjects could choose between them (studies 1-3). In study 1, dogs took several training trials to learn the discrimination between the generous and the selfish experimenters when both were of the same gender. In study 2, the discrimination was learned faster when the experimenters were of different gender as evidenced both by dogs' latencies to approach the bowl in training trials as well as by their choices in preference tests. Nevertheless, dogs did not get confused by gender when the experimenters were changed in between the training and the choice phase in study 3. We conclude that dogs spontaneously used human gender as a cue to discriminate between more and less cooperative experimenters. They also relied on some other personal feature which let them avoid being confused by gender when demonstrators were changed. We discuss these results in terms of dogs' ability to recognize individuals and the potential advantage of this skill for their lives in human environments.

Auditory evoked neuromagnetic fields of the primary and association auditory cortices were recorded while subjects learned to discriminate small differences in frequency and intensity between two consecutive tones. When discrimination was no better than chance, evoked field patterns across the scalp manifested no significant differences between correct and incorrect responses. However, when performance was correct on at least 75% of the trials, the spatial pattern of magnetic field differed significantly between correct and incorrect responses during the first 70 ms following the onset of the second tone. In this respect, the magnetic field pattern predicted when the subject would make an incorrect judgment more than 100 ms prior to indicating the judgment by a button press. One subject improved discrimination for much smaller differences between stimuli after 200 h of training. Evidence of cortical plasticity with improved discrimination is provided by an accompanying decrease of the relative magnetic field amplitude of the 100 ms response components in the primary and association auditory cortices.

Nurr1 expression is up-regulated in the brain following associative learning experiences, but its relevance to cognitive processes remains unclear. In these studies, rats initially received bilateral hippocampal infusions of control or antisense oligodeoxynucleotides (ODNs) 1 h prior to training in a holeboard spatial discrimination task. Such pre-training infusions of nurr1 antisense ODNs caused a moderate effect in learning the task and also impaired LTM tested 7 d later. In a second experi...

Nurr1 expression is up-regulated in the brain following associative learning experiences, but its relevance to cognitive processes remains unclear. In these studies, rats initially received bilateral hippocampal infusions of control or antisense oligodeoxynucleotides (ODNs) 1 h prior to training in a holeboard spatial discrimination task. Such pre-training infusions of nurr1 antisense ODNs caused a moderate effect in learning the task and also impaired LTM tested 7 d later. In a second experi...

unlabeled data, but does not necessarily produce features that are optimal for classification. In this paper we propose the convolutional classification restricted Boltzmann machine, which combines a generative and a discriminativelearning objective. This allows it to learn filters that are good both......The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann machines may...... outperform these standard filter banks because they learn a feature description directly from the training data. Like many other representation learning methods, restricted Boltzmann machines are unsupervised and are trained with a generative learning objective; this allows them to learn representations from...

Full Text Available Grading intraepithelial neoplasia is crucial to derive an accurate estimate of pre-cancerous stages and is currently performed by pathologists assessing histopathological images. Inter- and intra-observer variability can significantly be reduced, when reliable, quantitative image analysis is introduced into diagnostic processes. On a challenging dataset, we evaluated the potential of learning a classifier to grade anal intraepitelial neoplasia. Support vector machines were trained on images represented by fractal and statistical features. We show that pursuing a learning-based grading strategy yields highly reliable results. Compared to existing methods, the proposed method outperformed them by a significant margin.

Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer.

Nurr1 expression is up-regulated in the brain following associative learning experiences, but its relevance to cognitive processes remains unclear. In these studies, rats initially received bilateral hippocampal infusions of control or antisense oligodeoxynucleotides (ODNs) 1 hour prior to training in a holeboard spatial discrimination task. Such…

A discrimination reversal problem was presented to 192 children varying in age from 3 to 5 years. At the end of both the initial learning and transfer trials, probe trials were introduced to ascertain the response rule describing children's choices. (Author/SB)

The current study examined the impact of both the tendency to worry (trait worry) and the process of worry (state worry) on subsequent behavioral responding in a schedule discriminationlearning task. High and low trait worriers were randomly assigned to a state worry or relaxation incubation condition and completed a test of executive functioning…

Nurr1 expression is up-regulated in the brain following associative learning experiences, but its relevance to cognitive processes remains unclear. In these studies, rats initially received bilateral hippocampal infusions of control or antisense oligodeoxynucleotides (ODNs) 1 hour prior to training in a holeboard spatial discrimination task. Such…

Individuals with anxiety disorders often do not respond to safety signals and hence continue to be afraid and anxious. Consequently, it is important to develop paradigms in animals that can directly study brain systems involved in learning about, and responding to, safety signals. We previously developed a discrimination procedure in rats of the…

Full Text Available We have previously shown that olfactory discriminationlearning is accompanied by several forms of long-term enhancement in synaptic connections between layer II pyramidal neurons selectively in the piriform cortex. This study sought to examine whether the previously demonstrated olfactory-learning-task-induced modifications are preceded by suitable changes in the expression of mRNA for neurotrophic factors and in which brain areas this occurs. Rats were trained to discriminate positive cues in pair of odors for a water reward. The relationship between the learning task and local levels of mRNA for brain-derived neurotrophic factor, tyrosine kinase B, nerve growth factor, and neurotrophin-3 in the frontal cortex, hippocampal subregions, and other regions were assessed 24 hours post olfactory learning. The olfactory discriminationlearning activated production of endogenous neurotrophic factors and induced their signal transduction in the frontal cortex, but not in other brain areas. These findings suggest that different brain areas may be preferentially involved in different learning/memory tasks.

A long standing debate in cognitive neuroscience has been the extent to which perceptual processing is influenced by prior knowledge and experience with a task. A converging body of evidence now supports the view that a task does influence perceptual processing, leaving us with the challenge of understanding the locus of, and mechanisms underpinning, these influences. An exemplar of this influence is learned categorical perception (CP), in which there is superior perceptual discrimination of stimuli that are placed in different categories. Psychophysical experiments on humans have attempted to determine whether early cortical stages of visual analysis change as a result of learning a categorization task. However, while some results indicate that changes in visual analysis occur, the extent to which earlier stages of processing are changed is still unclear. To explore this issue, we develop a biologically motivated neural model of hierarchical vision processes consisting of a number of interconnected modules representing key stages of visual analysis, with each module learning to exhibit desired local properties through competition. With this system level model, we evaluate whether a CP effect can be generated with task influence to only the later stages of visual analysis. Our model demonstrates that task learning in just the later stages is sufficient for the model to exhibit the CP effect, demonstrating the existence of a mechanism that requires only a high-level of task influence. However, the effect generalizes more widely than is found with human participants, suggesting that changes to earlier stages of analysis may also be involved in the human CP effect, even if these are not fundamental to the development of CP. The model prompts a hybrid account of task-based influences on perception that involves both modifications to the use of the outputs from early perceptual analysis along with the possibility of changes to the nature of that early analysis itself

The ability of baboons to discriminate changes in the formant structures of a synthetic baboon grunt call and an acoustically similar human vowel (/eh/) was examined to determine how comparable baboons are to humans in discriminating small changes in vowel sounds, and whether or not any species-specific advantage in discriminability might exist when baboons discriminate their own vocalizations. Baboons were trained to press and hold down a lever to produce a pulsed train of a standard sound (e.g., /eh/ or a baboon grunt call), and to release the lever only when a variant of the sound occurred. Synthetic variants of each sound had the same first and third through fifth formants (F1 and F3-5), but varied in the location of the second formant (F2). Thresholds for F2 frequency changes were 55 and 67 Hz for the grunt and vowel stimuli, respectively, and were not statistically different from one another. Baboons discriminated changes in vowel formant structures comparable to those discriminated by humans. No distinct advantages in discrimination performances were observed when the baboons discriminated these synthetic grunt vocalizations.

valued temporal domain radio signals. We propose a method for accomplishing online semi-supervised learning with a tied- weight convolutional...reduction of X. Additionally, if φ is parameterized by a weight matrix W and ψ = W T , this method is known as weight tying and has the desirable...waveforms’ input. This binary data is modulated as in-phase and quadrature (I/Q) samples using each of six methods : on-off keying (OOK), Gaussian

It is generally believed that both sensory immaturity and inattention contribute to the poor listening of some children. However, the relative contribution of each factor, within and between individuals, and the nature of the inattention are poorly understood. In three experiments we examined the threshold and response variability of 6-11 y.o. children on pure tone frequency discrimination (FD) tasks. We first confirmed that younger children had both higher thresholds and greater within- and between-listener variability than older children and adults. Higher thresholds were mostly attributed to high response variability due to poor sustained attention. We next compared performance on the auditory FD task with that on visual spatial FD. No correlation was found between the thresholds or variability of individuals on the two tasks, suggesting involvement of modality-specific attention. Finally, we found lower thresholds for 8-9 y.o. children performing auditory FD training in a classroom than in the laboratory, possibly due to training session length or to a more familiar, motivating and focussed training environment. The adult-like performance of many younger children at times during their testing or training, together with the high response variability of immature performers, suggested that most elevated FD thresholds in children are due to inattention.

In this paper, a new linear dimension reduction method called supervised orthogonal discriminant subspace projection (SODSP) is proposed, which addresses high-dimensionality of data and the small sample size problem. More specifically, given a set of data points in the ambient space, a novel weight matrix that describes the relationship between the data points is first built. And in order to model the manifold structure, the class information is incorporated into the weight matrix. Based on the novel weight matrix, the local scatter matrix as well as non-local scatter matrix is defined such that the neighborhood structure can be preserved. In order to enhance the recognition ability, we impose an orthogonal constraint into a graph-based maximum margin analysis, seeking to find a projection that maximizes the difference, rather than the ratio between the non-local scatter and the local scatter. In this way, SODSP naturally avoids the singularity problem. Further, we develop an efficient and stable algorithm for implementing SODSP, especially, on high-dimensional data set. Moreover, the theoretical analysis shows that LPP is a special instance of SODSP by imposing some constraints. Experiments on the ORL, Yale, Extended Yale face database B and FERET face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of SODSP.

Learning to read involves discriminating between different written forms and establishing connections with phonology and semantics. This process may be partially built upon visual perceptual learning, during which the ability to process the attributes of visual stimuli progressively improves with practice. The present study investigated to what extent Chinese children with developmental dyslexia have deficits in perceptual learning by using a texture discrimination task, in which participants were asked to discriminate the orientation of target bars. Experiment l demonstrated that, when all of the participants started with the same initial stimulus-to-mask onset asynchrony (SOA) at 300 ms, the threshold SOA, adjusted according to response accuracy for reaching 80% accuracy, did not show a decrement over 5 days of training for children with dyslexia, whereas this threshold SOA steadily decreased over the training for the control group. Experiment 2 used an adaptive procedure to determine the threshold SOA for each participant during training. Results showed that both the group of dyslexia and the control group attained perceptual learning over the sessions in 5 days, although the threshold SOAs were significantly higher for the group of dyslexia than for the control group; moreover, over individual participants, the threshold SOA negatively correlated with their performance in Chinese character recognition. These findings suggest that deficits in visual perceptual processing and learning might, in part, underpin difficulty in reading Chinese.

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.

Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms but it can also dramatically reduce the time complexities in both training and testing phases.

Full Text Available BACKGROUND: Learning and perception of visual stimuli by free-flying honeybees has been shown to vary dramatically depending on the way insects are trained. Fine color discrimination is achieved when both a target and a distractor are present during training (differential conditioning, whilst if the same target is learnt in isolation (absolute conditioning, discrimination is coarse and limited to perceptually dissimilar alternatives. Another way to potentially enhance discrimination is to increase the penalty associated with the distractor. Here we studied whether coupling the distractor with a highly concentrated quinine solution improves color discrimination of both similar and dissimilar colors by free-flying honeybees. As we assumed that quinine acts as an aversive stimulus, we analyzed whether aversion, if any, is based on an aversive sensory input at the gustatory level or on a post-ingestional malaise following quinine feeding. METHODOLOGY/PRINCIPAL FINDINGS: We show that the presence of a highly concentrated quinine solution (60 mM acts as an aversive reinforcer promoting rejection of the target associated with it, and improving discrimination of perceptually similar stimuli but not of dissimilar stimuli. Free-flying bees did not use remote cues to detect the presence of quinine solution; the aversive effect exerted by this substance was mediated via a gustatory input, i.e. via a distasteful sensory experience, rather than via a post-ingestional malaise. CONCLUSION: The present study supports the hypothesis that aversion conditioning is important for understanding how and what animals perceive and learn. By using this form of conditioning coupled with appetitive conditioning in the framework of a differential conditioning procedure, it is possible to uncover discrimination capabilities that may remain otherwise unsuspected. We show, therefore, that visual discrimination is not an absolute phenomenon but can be modulated by experience.

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learningdiscriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

The purpose of this experiment was to establish discriminative control of responding by an antecedent stimulus using differential punishment because the results of past studies on this topic have been mixed. Three adults with mental retardation who exhibited stereotypy not maintained by social consequences (i.e., automatic reinforcement)…

In laboratory dogs, aging leads to a decline in various cognitive domains such as learning, memory and behavioural flexibility. However, much less is known about aging in pet dogs, i.e. dogs that are exposed to different home environments by their caregivers. We used tasks on a touchscreen apparatus to detect differences in various cognitive functions across pet Border Collies aged from 5 months to 13 years. Ninety-five dogs were divided into five age groups and tested in four tasks: (1) underwater photo versus drawing discrimination, (2) clip art picture discrimination, (3) inferential reasoning by exclusion and (4) a memory test with a retention interval of 6 months. The tasks were designed to test three cognitive abilities: visual discriminationlearning, logical reasoning and memory. The total number of sessions to reach criterion and the number of correction trials needed in the two discrimination tasks were compared across age groups. The results showed that both measures increased linearly with age, with dogs aged over 13 years displaying slower learning and reduced flexibility in comparison to younger dogs. Inferential reasoning ability increased with age, but less than 10 % of dogs showed patterns of choice consistent with inference by exclusion. No age effect was found in the long-term memory test. In conclusion, the discriminationlearning tests used are suitable to detect cognitive aging in pet dogs, which can serve as a basis for comparison to help diagnose cognition-related problems and as a tool to assist with the development of treatments to delay cognitive decline.

In many species, males and females have different reproductive roles and/or differ in their ecological niche. Since in these cases the two sexes often face different cognitive challenges, selection may promote some degree of cognitive differentiation, an issue that has received relatively little attention so far. We investigated the existence of sex differences in visual discriminationlearning in the guppy, Poecilia reticulata, a fish species in which females show complex mate choice based on male colour pattern. We tested males and females for their ability to learn a discrimination between two different shapes (experiment 1) and between two identical figures with a different orientation (experiment 2). In experiment 3, guppies were required to select an object of the odd colour in a group of five objects. Colours changed daily, and therefore, the solution for this task was facilitated by concept learning. We found males' and females' accuracy practically overlapped in the three experiments, suggesting that the two sexes have similar discriminationlearning abilities. Yet, males showed faster decision time than females without any evident speed-accuracy trade-off. This result indicates the existence of consistent between-sex differences in decision speed perhaps due to impulsivity rather than speed in information processing. Our results align with previous literature, indicating that sex differences in cognitive abilities are the exception rather than the rule, while sex differences in cognitive style, i.e. the way in which an individual faces a cognitive task, are much more common.

Full Text Available We show that human ability to discriminate the wavelength of monochromatic light can be understood as maximum likelihood decoding of the cone absorptions, with a signal processing efficiency that is independent of the wavelength. This work is built on the framework of ideal observer analysis of visual discrimination used in many previous works. A distinctive aspect of our work is that we highlight a perceptual confound that observers should confuse a change in input light wavelength with a change in input intensity. Hence a simple ideal observer model which assumes that an observer has a full knowledge of input intensity should over-estimate human ability in discriminating wavelengths of two inputs of unequal intensity. This confound also makes it difficult to consistently measure human ability in wavelength discrimination by asking observers to distinguish two input colors while matching their brightness. We argue that the best experimental method for reliable measurement of discrimination thresholds is the one of Pokorny and Smith, in which observers only need to distinguish two inputs, regardless of whether they differ in hue or brightness. We mathematically formulate wavelength discrimination under this wavelength-intensity confound and show a good agreement between our theoretical prediction and the behavioral data. Our analysis explains why the discrimination threshold varies with the input wavelength, and shows how sensitively the threshold depends on the relative densities of the three types of cones in the retina (and in particular predict discriminations in dichromats. Our mathematical formulation and solution can be applied to general problems of sensory discrimination when there is a perceptual confound from other sensory feature dimensions.

Feature extraction and learning is critical for object recognition and detection. By embedding context cue of image attributes into the kernel descriptors, we propose a set of novel kernel descriptors called context kernel descriptors (CKD). The motivation of CKD is to use the spatial consistency...... codebook and reduced CKD are discriminative. We report superior performance of our algorithm for object recognition on benchmark datasets like Caltech-101 and CIFAR-10, as well as for detection on a challenging chicken feet dataset....

The cholinergic system has extensive projections to the olfactory bulb (OB) where it produces a state-dependent regulation of sensory gating. Previous work has shown a prominent role of muscarinic acetylcholine (ACh) receptors (mAChRs) in regulating the excitability of OB neurons, in particular the M1 receptor. Here, we examined the contribution of M1 and M3 mAChR subtypes to olfactory processing using mice with a genetic deletion of these receptors, the M1−/− and the M1/M3−/− knockout (KO) mice. Genetic ablation of the M1 and M3 mAChRs resulted in a significant deficit in odor discrimination of closely related molecules, including stereoisomers. However, the discrimination of dissimilar molecules, social odors (e.g., urine) and novel object recognition was not affected. In addition the KO mice showed impaired learning in an associative odor-learning task, learning to discriminate odors at a slower rate, indicating that both short and long-term memory is disrupted by mAChR dysfunction. Interestingly, the KO mice exhibited decreased olfactory neurogenesis at younger ages, a deficit that was not maintained in older animals. In older animals, the olfactory deficit could be restored by increasing the number of new born neurons integrated into the OB after exposing them to an olfactory enriched environment, suggesting that muscarinic modulation and adult neurogenesis could be two different mechanism used by the olfactory system to improve olfactory processing. PMID:28210219

Pavlovian conditioning is an elementary form of reward-related behavioral adaptation. The mesolimbic dopamine system is widely considered to mediate critical aspects of reward-related learning. For example, initial acquisition of positively-reinforced operant behavior requires dopamine (DA) D1 receptor (D1R) activation in the basolateral amygdala (BLA), central nucleus of the amygdala (CeA), and the ventral subiculum (vSUB). However, the role of D1R activation in these areas on appetitive, non-drug-related, Pavlovian learning is not currently known. In separate experiments, microinfusions of the D1-like receptor antagonist SCH-23390 (3.0 nmol/0.5 μL per side) into the amygdala and subiculum preceded discriminated Pavlovian conditioned approach (dPCA) training sessions. D1-like antagonism in all three structures impaired the acquisition of discriminated approach, but had no effect on performance after conditioning was asymptotic. Moreover, dissociable effects of D1-like antagonism in the three structures on components of discriminated responding were obtained. Lastly, the lack of latent inhibition in drug-treated groups may elucidate the role of D1-like in reward-related Pavlovian conditioning. The present data suggest a role for the D1 receptors in the amygdala and hippocampus in learning the significance of conditional stimuli, but not in the expression of conditional responses. PMID:26632336

Pavlovian conditioning is an elementary form of reward-related behavioral adaptation. The mesolimbic dopamine system is widely considered to mediate critical aspects of reward-related learning. For example, initial acquisition of positively-reinforced operant behavior requires dopamine (DA) D1 receptor (D1R) activation in the basolateral amygdala (BLA), central nucleus of the amygdala (CeA), and the ventral subiculum (vSUB). However, the role of D1R activation in these areas on appetitive, non-drug-related, Pavlovian learning is not currently known. In separate experiments, microinfusions of the D1-like receptor antagonist SCH-23390 (3.0 nmol/0.5 μL per side) into the amygdala and subiculum preceded discriminated Pavlovian conditioned approach (dPCA) training sessions. D1-like antagonism in all three structures impaired the acquisition of discriminated approach, but had no effect on performance after conditioning was asymptotic. Moreover, dissociable effects of D1-like antagonism in the three structures on components of discriminated responding were obtained. Lastly, the lack of latent inhibition in drug-treated groups may elucidate the role of D1-like in reward-related Pavlovian conditioning. The present data suggest a role for the D1 receptors in the amygdala and hippocampus in learning the significance of conditional stimuli, but not in the expression of conditional responses.

Disorders of the heart valves constitute a considerable health problem and often require surgical intervention. Recently various approaches were published seeking to overcome the shortcomings of current clinical practice,that still relies on manually performed measurements for performance assessment. Clinical decisions are still based on generic information from clinical guidelines and publications and personal experience of clinicians. We present a framework for retrieval and decision support using learning based discriminative distance functions and visualization of patient similarity with relative neighborhood graphsbased on shape and derived features. We considered two learning based techniques, namely learning from equivalence constraints and the intrinsic Random Forest distance. The generic approach enables for learning arbitrary user-defined concepts of similarity depending on the application. This is demonstrated with the proposed applications, including automated diagnosis and interventional suitability classification, where classification rates of up to 88.9% and 85.9% could be observed on a set of valve models from 288 and 102 patients respectively.

The automatic analysis of human motion from images opens up the way for applications in the domains of security and surveillance, human-computer interaction, animation, retrieval and sports motion analysis. In this dissertation, the focus is on robust and fast human pose recovery and action recognit

Full Text Available Previous research has shown age related differences in discriminating motion at different levels of contrast (Betts, Sekuler, & Bennett, 2009; 2012; Betts, Taylor, Sekuler & Bennett, 2005. A surprising result of this research is that older as compared to younger observers showed improved performance in detecting motion of large high contrast stimuli suggesting age-related differences in center-surround antagonism. In the present study we examined whether perceptual learning methods could be used to improve motion discrimination performance for older individuals under high- and low-contrast conditions. The stimuli were centrally presented Gaussian filtered sine-wave gratings (Gabors that were either 5 or 0.7 degree diameter with contrast of 0.92, 0.22, or 0.028. Older and younger participants received 3 days of training. The task was to identify if the motion direction was leftward or rightward. Duration thresholds for motion discrimination were derived using two randomly interleaved staircases and compared between pre-/post- test sessions. Both older and younger subjects showed lower duration thresholds as a result of training. The improved performance, for older subjects, due to training was observed for all size and contrast conditions, with training with small low-contrast stimuli resulting in a 23% improvement in motion discrimination performance. Older observers, as compared to younger observers, did show evidence of decreased spatial suppression across all contrast levels. These results suggest that perceptual learning techniques are effective for improving motion discrimination performance, especially for conditions that are difficult for older individuals.

Learning theories distinguish elemental from configural learning based on their different complexity. Although the former relies on simple and unambiguous links between the learned events, the latter deals with ambiguous discriminations in which conjunctive representations of events are learned as being different from their elements. In mammals, configural learning is mediated by brain areas that are either dispensable or partially involved in elemental learning. We studied whether the insect brain follows the same principles and addressed this question in the honey bee, the only insect in which configural learning has been demonstrated. We used a combination of conditioning protocols, disruption of neural activity, and optophysiological recording of olfactory circuits in the bee brain to determine whether mushroom bodies (MBs), brain structures that are essential for memory storage and retrieval, are equally necessary for configural and elemental olfactory learning. We show that bees with anesthetized MBs distinguish odors and learn elemental olfactory discriminations but not configural ones, such as positive and negative patterning. Inhibition of GABAergic signaling in the MB calyces, but not in the lobes, impairs patterning discrimination, thus suggesting a requirement of GABAergic feedback neurons from the lobes to the calyces for nonelemental learning. These results uncover a previously unidentified role for MBs besides memory storage and retrieval: namely, their implication in the acquisition of ambiguous discrimination problems. Thus, in insects as in mammals, specific brain regions are recruited when the ambiguity of learning tasks increases, a fact that reveals similarities in the neural processes underlying the elucidation of ambiguous tasks across species.

The effect of excitotoxic lesions of the hippocampus on acquisition and reversal of simple and conditional tasks was investigated using a Y-maze. Hippocampal-lesioned rats were severely impaired on acquisition and reversal of a conditional visuo-spatial task (where different pairs of visually distinctive choice arms indicated whether a left or right arm choice was correct on that trial) and were unable to acquire a visuo-visual conditional discrimination (where the appearance of the start arm indicated which of the visually distinctive choice arms was correct irrespective of their left/right position). They were not impaired on acquisition or reversal of a simple spatial left/right discrimination task (where all arms had the same visual appearance) nor on acquisition of a visual discrimination (where the correct, visually distinctive, choice arm varied in its left/right position). Hippocampal-lesioned rats were, however, impaired on reversal of this visual discrimination task and on acquisition and reversal of another visual discrimination task in which the visually distinctive choice arms were less different from each other than in the first version of this task. The degree of impairment in the lesioned rats was related to task difficulty for the sham-operated rats and was not specific to tasks requiring spatial choices, visual discrimination or conditional responding. The impairment on conditional tasks was greater than the impairment on those non-conditional tasks which happened to be matched for task difficulty for the sham-operated rats, suggesting that the conditional demand may target the function of the hippocampus rather closely. Statistically worse than chance performance by hippocampal-lesioned (and sham-operated) rats at the beginning of reversal testing, which was given 24 h after achieving criterion on acquisition of that task, indicated that hippocampal-lesioned rats simultaneously exhibited good memory but impaired learning for the type of

Managing human resource learning for innovation develops a systemic understanding of building innovative capabilities. Building innovative capabilities require active creation, coordination and absorption of useful knowledge and thus a cohesive management approach to learning. Often learning...... in organizations and work is approached without considerations on how to integrate it in the management of human resources. The book investigates the empirical conditions for managing human resources learning for innovation. With focus on innovative performance the importance of modes of innovation, clues...

Two methods investigating learning and memory in juvenile Gottingen minipigs were evaluated for potential use in preclinical toxicity testing. Twelve minipigs were tested using a spatial hole-board discrimination test including a learning phase and two memory phases. Five minipigs were tested...... in a visual discrimination test. The juvenile minipigs were able to learn the spatial hole-board discrimination test and showed improved working and reference memory during the learning phase. Performance in the memory phases was affected by the retention intervals, but the minipigs were able to remember...... the concept of the test in both memory phases. Working memory and reference memory were significantly improved in the last trials of the memory phases. In the visual discrimination test, the minipigs learned to discriminate between the three figures presented to them within 9-14 sessions. For the memory test...

The rapid evaluation of complex visual environments is critical for an organism's adaptation and survival. Previous studies have shown that emotionally significant visual scenes, both pleasant and unpleasant, elicit a larger late positive wave in the event-related brain potential (ERP) than emotionally neutral pictures. The purpose of the present study was to examine whether neuroelectric responses elicited by complex pictures discriminate between specific, biologically relevant contents of the visual scene and to determine how early in the picture processing this discrimination occurs. Subjects (n=264) viewed 55 color slides differing in both scene content and emotional significance. No categorical judgments or responses were required. Consistent with previous studies, we found that emotionally arousing pictures, regardless of their content, produce a larger late positive wave than neutral pictures. However, when pictures were further categorized by content, anterior ERP components in a time window between 200−600 ms following stimulus onset showed a high selectivity for pictures with erotic content compared to other pictures regardless of their emotional valence (pleasant, neutral, and unpleasant) or emotional arousal. The divergence of ERPs elicited by erotic and non-erotic contents started at 185 ms post-stimulus in the fronto-central midline regions, with a later onset in parietal regions. This rapid, selective, and content-specific processing of erotic materials and its dissociation from other pictures (including emotionally positive pictures) suggests the existence of a specialized neural network for prioritized processing of a distinct category of biologically relevant stimuli with high adaptive and evolutionary significance. PMID:16712815

The aim of the present study was to examine a potential mechanism of action of gabapentin to manage cannabis-use disorders by determining the interoceptive effects of gabapentin in cannabis users discriminating [INCREMENT]-tetrahydrocannabinol ([INCREMENT]-THC) using a pharmacologically selective drug-discrimination procedure. Eight cannabis users learned to discriminate 30 mg oral [INCREMENT]-THC from placebo and then received gabapentin (600 and 1200 mg), [INCREMENT]-THC (5, 15, and 30 mg), and placebo alone and in combination. Self-report, task performance, and physiological measures were also collected. [INCREMENT]-THC served as a discriminative stimulus, produced positive subjective effects, elevated heart rate, and impaired psychomotor performance. Both doses of gabapentin substituted for the [INCREMENT]-THC discriminative stimulus and engendered subjective and performance-impairing effects that overlapped with those of [INCREMENT]-THC when administered alone. When administered concurrently, gabapentin shifted the discriminative-stimulus effects of [INCREMENT]-THC leftward/upward, and combinations of [INCREMENT]-THC and gabapentin generally produced larger effects on cannabinoid-sensitive outcomes relative to [INCREMENT]-THC alone. These results suggest that one mechanism by which gabapentin might facilitate cannabis abstinence is by producing effects that overlap with those of cannabinoids.

Honeybees learn color information of rewarding flowers and recall these memories in future decisions. For fine color discrimination, bees require differential conditioning with a concurrent presentation of target and distractor stimuli to form a long-term memory. Here we investigated whether the long-term storage of color information shapes the neural network of microglomeruli in the mushroom body calyces and if this depends on the type of conditioning. Free-flying honeybees were individually trained to a pair of perceptually similar colors in either absolute conditioning towards one of the colors or in differential conditioning with both colors. Subsequently, bees of either conditioning groups were tested in non-rewarded discrimination tests with the two colors. Only bees trained with differential conditioning preferred the previously learned color, whereas bees of the absolute conditioning group, and a stimuli-naïve group, chose randomly among color stimuli. All bees were then kept individually for three days in the dark to allow for complete long-term memory formation. Whole-mount immunostaining was subsequently used to quantify variation of microglomeruli number and density in the mushroom-body lip and collar. We found no significant differences among groups in neuropil volumes and total microglomeruli numbers, but learning performance was negatively correlated with microglomeruli density in the absolute conditioning group. Based on these findings we aim to promote future research approaches combining behaviorally relevant color learning tests in honeybees under free-flight conditions with neuroimaging analysis; we also discuss possible limitations of this approach.

This paper proposes a novel depth-aware salient object detection and segmentation framework via multiscale discriminative saliency fusion (MDSF) and bootstrap learning for RGBD images (RGB color images with corresponding Depth maps) and stereoscopic images. By exploiting low-level feature contrasts, mid-level feature weighted factors and high-level location priors, various saliency measures on four classes of features are calculated based on multiscale region segmentation. A random forest regressor is learned to perform the discriminative saliency fusion (DSF) and generate the DSF saliency map at each scale, and DSF saliency maps across multiple scales are combined to produce the MDSF saliency map. Furthermore, we propose an effective bootstrap learning-based salient object segmentation method, which is bootstrapped with samples based on the MDSF saliency map and learns multiple kernel support vector machines. Experimental results on two large datasets show how various categories of features contribute to the saliency detection performance and demonstrate that the proposed framework achieves the better performance on both saliency detection and salient object segmentation.

Discriminative dictionary learning (DDL) framework has been widely used in image classification which aims to learn some class-specific feature vectors as well as a representative dictionary according to a set of labeled training samples. However, interclass similarities and intraclass variances among input samples and learned features will generally weaken the representability of dictionary and the discrimination of feature vectors so as to degrade the classification performance. Therefore, how to explicitly represent them becomes an important issue. In this paper, we present a novel DDL framework with two-level low rank and group sparse decomposition model. In the first level, we learn a class-shared and several class-specific dictionaries, where a low rank and a group sparse regularization are, respectively, imposed on the corresponding feature matrices. In the second level, the class-specific feature matrix will be further decomposed into a low rank and a sparse matrix so that intraclass variances can be separated to concentrate the corresponding feature vectors. Extensive experimental results demonstrate the effectiveness of our model. Compared with the other state-of-the-arts on several popular image databases, our model can achieve a competitive or better performance in terms of the classification accuracy.

To deal with the problem of insufficient labeled data in video object classification, one solution is to utilize additional pairwise constraints that indicate the relationship between two examples, i.e., whether these examples belong to the same class or not. In this paper, we propose a discriminativelearning approach which can incorporate pairwise constraints into a conventional margin-based learning framework. Different from previous work that usually attempts to learn better distance metrics or estimate the underlying data distribution, the proposed approach can directly model the decision boundary and, thus, require fewer model assumptions. Moreover, the proposed approach can handle both labeled data and pairwise constraints in a unified framework. In this work, we investigate two families of pairwise loss functions, namely, convex and nonconvex pairwise loss functions, and then derive three pairwise learning algorithms by plugging in the hinge loss and the logistic loss functions. The proposed learning algorithms were evaluated using a people identification task on two surveillance video data sets. The experiments demonstrated that the proposed pairwise learning algorithms considerably outperform the baseline classifiers using only labeled data and two other pairwise learning algorithms with the same amount of pairwise constraints.

The present studies examined the role of linguistic experience in directing English and Mandarin learners' attention to aspects of a visual scene. Specifically, they asked whether young language learners in these 2 cultures attend to differential aspects of a word-learning situation. Two groups of English and Mandarin learners, 6-8-month-olds (n = 65) and 17-19-month-olds (n = 91), participated in 2 studies, based on a habituation paradigm, designed to test infants' discrimination between actions and objects in dynamic events. In Study 1, these stimuli were presented in silence, whereas in Study 2, a verbal label accompanied videos. Results showed that 6-8-month-olds could discriminate action changes but not object changes, whereas 17-19-month-olds could discriminate both types of changes. However, there were only very subtle cross-linguistic differences in these patterns when the scenes were presented together with a verbal label. These findings show strong evidence for universal developmental trends in attention, with somewhat weaker evidence that the differences in the types of words Mandarin- versus English-learning children produce or are exposed to affect attention to different aspects of a scene in the first 2 years of life. (c) 2015 APA, all rights reserved).

The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

Deviations in reward sensitivity and behavioral flexibility, particularly in the ability to change or stop behaviors in response to changing environmental contingencies, are important phenotypic dimensions of several neuropsychiatric disorders. Neuroimaging evidence suggests that variation in dopamine signaling through dopamine D(2)-like receptors may influence these phenotypes, as well as associated psychiatric conditions, but the specific neurocognitive mechanisms through which this influence is exerted are unknown. To address this question, we examined the relationship between behavioral sensitivity to reinforcement during discriminationlearning and D(2)-like receptor availability in vervet monkeys. Monkeys were assessed for their ability to acquire, retain, and reverse three-choice, visual-discrimination problems, and once behavioral performance had stabilized, they received positron emission tomography (PET) scans. D(2)-like receptor availability in dorsal aspects of the striatum was not related to individual differences in the ability to acquire or retain visual discriminations but did relate to the number of trials required to reach criterion in the reversal phase of the task. D(2)-like receptor availability was also strongly correlated with behavioral sensitivity to positive, but not negative, feedback during learning. These results go beyond electrophysiological findings by demonstrating the involvement of a striatal dopaminergic marker in individual differences in feedback sensitivity and behavioral flexibility, providing insight into the neural mechanisms that are affected in neuropsychiatric disorders that feature these deficits.

Although domestic dogs can respond to many facial cues displayed by other dogs and humans, it remains unclear whether they can differentiate individual dogs or humans based on facial cues alone and, if so, whether they would demonstrate the face inversion effect, a behavioural hallmark commonly used in primates to differentiate face processing from object processing. In this study, we first established the applicability of the visual paired comparison (VPC or preferential looking) procedure for dogs using a simple object discrimination task with 2D pictures. The animals demonstrated a clear looking preference for novel objects when simultaneously presented with prior-exposed familiar objects. We then adopted this VPC procedure to assess their face discrimination and inversion responses. Dogs showed a deviation from random behaviour, indicating discrimination capability when inspecting upright dog faces, human faces and object images; but the pattern of viewing preference was dependent upon image category. They directed longer viewing time at novel (vs. familiar) human faces and objects, but not at dog faces, instead, a longer viewing time at familiar (vs. novel) dog faces was observed. No significant looking preference was detected for inverted images regardless of image category. Our results indicate that domestic dogs can use facial cues alone to differentiate individual dogs and humans and that they exhibit a non-specific inversion response. In addition, the discrimination response by dogs of human and dog faces appears to differ with the type of face involved.

The objective of the present study is to propose and evaluate a novel multivariate approach for genetic mapping of complex categorical diseases. This approach results from an application of standard stepwise discriminant analysis to detect linkage based on the differential marker identity-by-descent (IBD) distributions among the different groups of sib pairs. Two major advantages of this method are that it allows for simultaneously testing all markers, together with other genetic and environmental factors in a single multivariate setting and it avoids explicitly modeling the complex relationship between the affection status of sib pairs and the underlying genetic determinants. The efficiency and properties of the method are demonstrated via simulations. The proposed multivariate approach has successfully located the true position(s) under various genetic scenarios. The more important finding is that using highly densely spaced markers (1～2 cM) leads to only a marginal loss of statistical efficiency of the proposed methods in terms of gene localization and statistical power. These results have well established its utility and advantages as a fine-mapping tool. A unique property of the proposed method is the ability to map multiple linked trait loci to their precise positions due to its sequential nature, as demonstrated via simulations.

Sound units play a pivotal role in cognitive models of auditory comprehension. The general consensus is that during perception listeners break down speech into auditory words and subsequently phones. Indeed, cognitive speech recognition is typically taken to be computationally intractable without phones. Here we present a computational model trained on 20 hours of conversational speech that recognizes word meanings within the range of human performance (model 25%, native speakers 20-44%), without making use of phone or word form representations. Our model also generates successfully predictions about the speed and accuracy of human auditory comprehension. At the heart of the model is a 'wide' yet sparse two-layer artificial neural network with some hundred thousand input units representing summaries of changes in acoustic frequency bands, and proxies for lexical meanings as output units. We believe that our model holds promise for resolving longstanding theoretical problems surrounding the notion of the phone in linguistic theory.

It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.

Human music perception is related both to musical experience and the physical properties of sound. Examining the processing of music by nonhuman animals has been generally neglected. We tested both black-capped chickadees and humans in a chord discrimination task that replicates and extends prior research with pigeons. We found that chickadees and humans, in common with pigeons, showed similar patterns of discrimination across manipulations of the 3rd and 5th notes of the triadic chords. For all species (chickadee and humans here, pigeons previously), chords with half-step alterations in the 5th note were easier to discriminate than half-step manipulations of the 3rd note, which is likely due to the sensory consonance of these chords. There were differences among species in terms of the fine discrimination of the chords within this larger pattern of results. Further, the ability to relearn the chords when transposed to a new root differed across species. Our results provide new comparative data suggesting some similarities in chord perception that span a wide range of species, from pigeons (nonvocal learners) to songbirds and humans (vocal learners).

Full Text Available Animals learn to choose a proper action among alternatives to improve their odds of success in food foraging and other activities critical for survival. Through trial-and-error, they learn correct associations between their choices and external stimuli. While a neural network that underlies such learning process has been identified at a high level, it is still unclear how individual neurons and a neural ensemble adapt as learning progresses. In this study, we monitored the activity of single units in the rat medial and lateral agranular (AGm and AGl, respectively areas as rats learned to make a left or right side lever press in response to a left or right side light cue. We noticed that rat movement parameters during the performance of the directional choice task quickly became stereotyped during the first 2-3 days or sessions. But learning the directional choice problem took weeks to occur. Accompanying rats’ behavioral performance adaptation, we observed neural modulation by directional choice in recorded single units. Our analysis shows that ensemble mean firing rates in the cue-on period did not change significantly as learning progressed, and the ensemble mean rate difference between left and right side choices did not show a clear trend of change either. However, the spatiotemporal firing patterns of the neural ensemble exhibited improved discriminability between the two directional choices through learning. These results suggest a spatiotemporal neural coding scheme in a motor cortical neural ensemble that may be responsible for and contributing to learning the directional choice task.

Measuring gender inequality and women's empowerment is essential to understand the determinants of gender gaps, evaluate policies and monitor countries' progress. With this aim, over the past two decades, research has mainly been directed towards the development of composite indices. The purpose of this paper is to introduce a new and interdisciplinary perspective to the current debate on measuring gender inequality in human development. As a starting point, we develop a simple macroeconomic model of the interdependence between human development and gender inequality. We then introduce a biometric indicator, based on the ratio of female to male body mass index, to measure women's empowerment at the country level. Finally, by using the latest available data, we examine the ability of this biometric indicator to capture countries' performance in achieving gender equality. We obtain five main results: 1) we provide a theoretical framework to explain the joint determination of human development and gender inequality; 2) we show how to use this framework to simulate the impact of exogenous shocks or policy changes; 3) we demonstrate that exogenous changes have a direct and a multiplier effect on human development and gender inequality; 4) we find that the distribution of obesity between the female and male populations represents a useful proxy variable for measuring gender equality at the country level; 5) finally, we use these results to integrate and develop existing knowledge on the 'ecological' approach to the overweight and obesity pandemic.

Guo, Yanrong; Shao, Yeqin [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong; Price, True [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Oto, Aytekin [Department of Radiology, Section of Urology, University of Chicago, Illinois 60637 (United States); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

2014-07-15

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on

We trained rhesus monkeys on six visual discrimination problems using stimuli that varied in both shape and colour. For one group of animals shape was always relevant in these six problems, and colour always irrelevant, and for the other animals vice versa. During these “intradimensional shifts” (ID) the problems were learned at equal rates by the two groups, shape-relevant and colour-relevant. We then trained three further problems in which the other dimension was now relevant (“extradimensi...

Structures in the medial temporal lobe, including the hippocampus and perirhinal cortex, are known to be essential for the formation of long-term memory. Recent animal and human studies have investigated whether perirhinal cortex might also be important for visual perception. In our study, using a simultaneous oddity discrimination task, rats with…

Structures in the medial temporal lobe, including the hippocampus and perirhinal cortex, are known to be essential for the formation of long-term memory. Recent animal and human studies have investigated whether perirhinal cortex might also be important for visual perception. In our study, using a simultaneous oddity discrimination task, rats with…

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plant's resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis' chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds (λ = 280-400ηm), suggesting that besides the biological activities of those

Full Text Available In behavioural experiments, motivation to learn can be achieved using food rewards as positive reinforcement in food-restricted animals. Previous studies reduce animal weights to 80-90% of free-feeding body weight as the criterion for food restriction. However, effects of different degrees of food restriction on task performance have not been assessed. We compared learning task performance in mice food-restricted to 80 or 90% body weight (BW. We used adult wildtype (WT; C57Bl/6j and knockout (ephrin-A2⁻/⁻ mice, previously shown to have a reverse learning deficit. Mice were trained in a two-choice visual discrimination task with food reward as positive reinforcement. When mice reached criterion for one visual stimulus (80% correct in three consecutive 10 trial sets they began the reverse learning phase, where the rewarded stimulus was switched to the previously incorrect stimulus. For the initial learning and reverse phase of the task, mice at 90%BW took almost twice as many trials to reach criterion as mice at 80%BW. Furthermore, WT 80 and 90%BW groups significantly differed in percentage correct responses and learning strategy in the reverse learning phase, whereas no differences between weight restriction groups were observed in ephrin-A2⁻/⁻ mice. Most importantly, genotype-specific differences in reverse learning strategy were only detected in the 80%BW groups. Our results indicate that increased food restriction not only results in better performance and a shorter training period, but may also be necessary for revealing behavioural differences between experimental groups. This has important ethical and animal welfare implications when deciding extent of diet restriction in behavioural studies.

Recent proposals have conceptualized piriform cortex as an association cortex, capable of integrating incoming olfactory information with descending input from higher order associative regions such as orbitofrontal cortex (OFC). If true, encoding in piriform cortex should reflect associative features prominent in these areas during associative learning involving olfactory cues. To test this hypothesis, we recorded from neurons in OFC and anatomically related parts of the anterior piriform cortex (APC) in rats, learning and reversing novel odor discriminations. Findings in OFC were similar to what we have reported previously, with nearly all the cue-selective neurons exhibiting substantial plasticity during learning and reversal. Also, many of the cue-selective neurons were originally responsive in anticipation of the outcomes early in learning, thereby providing a single-unit representation of the cue-outcome associations. Some of these features were also evident in firing activity in APC, including some plasticity across learning and reversal. However, APC neurons failed to reverse cue selectivity when the associated outcome was changed, and the cue-selective population did not include neurons that were active prior to outcome delivery. Thus, although representations in APC are substantially more associative than expected in a purely sensory region, they do appear to be somewhat more constrained by the sensory features of the odor cues than representations in downstream areas of OFC. PMID:16699083

Development of a prototype 3-D through-wall synthetic aperture radar (SAR) system is currently underway at Defence Research and Development Canada. The intent is to map out building wall layouts and to detect targets of interest and their location behind walls such as humans, arms caches, and furniture. This situational awareness capability can be invaluable to the military working in an urban environment. Tools and algorithms are being developed to exploit the resulting 3-D imagery. Current work involves analyzing signatures of targets behind a wall and understanding the clutter and multipath signals in a room of interest. In this paper, a comprehensive study of 3-D human target signature metrics in free space is presented. The aim is to identify features for discrimination of the human target from other targets. Targets used in this investigation include a human standing, a human standing with arms stretched out, a chair, a table, and a metallic plate. Several features were investigated as potential discriminants and five which were identified as good candidates are presented in this paper. Based on this study, no single feature could be used to fully discriminate the human targets from all others. A combination of at least two different features is required to achieve this.

Humans recognize body parts in categories. Previous studies have shown that responses in the fusiform body area (FBA) and extrastriate body area (EBA) are evoked by the perception of the human body, when presented either as whole or as isolated parts. These responses occur approximately 190 ms after body images are visualized. The extent to which body-sensitive responses show specificity for different body part categories remains to be largely clarified. We used a decoding method to quantify neural responses associated with the perception of different categories of body parts. Nine subjects underwent measurements of their brain activities by magnetoencephalography (MEG) while viewing 14 images of feet, hands, mouths, and objects. We decoded categories of the presented images from the MEG signals using a support vector machine (SVM) and calculated their accuracy by 10-fold cross-validation. For each subject, a response that appeared to be a body-sensitive response was observed and the MEG signals corresponding to the three types of body categories were classified based on the signals in the occipitotemporal cortex. The accuracy in decoding body-part categories (with a peak at approximately 48%) was above chance (33.3%) and significantly higher than that for random categories. According to the time course and location, the responses are suggested to be body-sensitive and to include information regarding the body-part category. Finally, this non-invasive method can decode category information of a visual object with high temporal and spatial resolution and this result may have a significant impact in the field of brain-machine interface research.

The dorsal striatum in basal ganglia circuit mediates learning processes contributing to instrumental motor actions. The striatum receives excitatory inputs from many cortical areas and the thalamic nuclei and dopaminergic inputs from the ventral midbrain and projects to the output nuclei through direct and indirect pathways. The neural mechanism remains unclear as to how these striatofugal pathways control the learning processes of instrumental actions. Here, we addressed the behavioral roles of the two striatofugal pathways in the performance of sensory discrimination by using immunotoxin (IT)-mediated cell targeting. IT targeting of the striatal direct pathway in mutant mice lengthened the response time but did not affect the accuracy of the response selection in visual discrimination. Subregion-specific pathway targeting revealed a delay in motor responses generated by elimination of the direct pathway arising from the dorsomedial striatum (DMS) but not from the dorsolateral striatum (DLS). These findings indicate that the direct pathway, in particular that from the DMS, contributes to the regulation of the response time in visual discrimination. In addition, IT targeting of the striatal indirect pathway originating from the DLS in transgenic rats impaired the accuracy of response selection in auditory discrimination, whereas the response time remained normal. These data demonstrate that the DLS-derived indirect pathway plays an essential role in the control of the selection accuracy of learned motor responses. Our results suggest that striatal direct and indirect pathways act cooperatively to regulate the selection accuracy and response time of learned motor actions in the performance of discriminativelearning.

Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mechanisms underlying AH in schizophrenia may offer insight into the pathophysiology associated with AH more broadly across multiple neuropsychiatric disease conditions. In this paper, we address the problem of classifying schizophrenia patients with and without a history of AH, and healthy control (HC) subjects. To this end, we performed feature extraction from resting state functional magnetic resonance imaging (rsfMRI) data and applied machine learning classifiers, testing two kinds of neuroimaging features: (a) functional connectivity (FC) measures computed by lattice auto-associative memories (LAAM), and (b) local activity (LA) measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF). We show that it is possible to perform classification within each pair of subject groups with high accuracy. Discrimination between patients with and without lifetime AH was highest, while discrimination between schizophrenia patients and HC participants was worst, suggesting that classification according to the symptom dimension of AH may be more valid than discrimination on the basis of traditional diagnostic categories. FC measures seeded in right Heschl's gyrus (RHG) consistently showed stronger discriminative power than those seeded in left Heschl's gyrus (LHG), a finding that appears to support AH models focusing on right hemisphere abnormalities. The cortical brain localizations derived from the features with strong classification performance are consistent with proposed AH models, and include left inferior frontal gyrus (IFG), parahippocampal gyri, the cingulate cortex, as well as several temporal and prefrontal cortical brain regions. Overall, the observed findings suggest that

This paper presents a new supervised learning framework for the efficient recognition and segmentation of anatomical structures in 3D computed tomography (CT), with as little training data as possible. Training supervised classifiers to recognize organs within CT scans requires a large number of manually delineated exemplar 3D images, which are very expensive to obtain. In this study, we borrow ideas from the field of active learning to optimally select a minimum subset of such images that yields accurate anatomy segmentation. The main contribution of this work is in designing a combined generative-discriminative model which: i) drives optimal selection of training data; and ii) increases segmentation accuracy. The optimal training set is constructed by finding unlabeled scans which maximize the disagreement between our two complementary probabilistic models, as measured by a modified version of the Jensen-Shannon divergence. Our algorithm is assessed on a database of 196 labeled clinical CT scans with high variability in resolution, anatomy, pathologies, etc. Quantitative evaluation shows that, compared with randomly selecting the scans to annotate, our method decreases the number of training images by up to 45%. Moreover, our generative model of body shape substantially increases segmentation accuracy when compared to either using the discriminative model alone or a generic smoothness prior (e.g. via a Markov Random Field).

Full Text Available Avoidance is considered as a central hallmark of all anxiety disorders. The acquisition and expression of avoidance which leads to the maintenance and exacerbation of pathological fear is closely linked to Pavlovian and operant conditioning processes. Changes in conditionability might represent a key feature of all anxiety disorders but the exact nature of these alterations might vary across different disorders. To date, no information is available on specific changes in conditionability for disorder-irrelevant stimuli in specific phobia (SP. The first aim of this study was to investigate changes in fear acquisition and extinction in spider-fearful individuals as compared to non-fearful participants by using the de novo fear conditioning paradigm. Secondly, we aimed to determine whether differences in the magnitude of context-dependent fear retrieval exist between spider-fearful and non-fearful individuals. Our findings point to an enhanced fear discrimination in spider-fearful individuals as compared to non-fearful individuals at both the physiological and subjective level. The enhanced fear discrimination in spider-fearful individuals was neither mediated by increased state anxiety, depression, nor stress tension. Spider-fearful individuals displayed no changes in extinction learning and/or fear retrieval. Surprisingly, we found no evidence for context-dependent modulation of fear retrieval in either group. Here we provide first evidence that spider-fearful individuals show an enhanced discriminative fear learning of phobia-irrelevant (de novo stimuli. Our findings provide novel insights into the role of fear acquisition and expression for the development and maintenance of maladaptive responses in the course of SP.

Stigma and discrimination constitute one of the greatest barriers to dealing effectively with the HIV epidemic, underlying a range of human rights violations and hindering access to prevention, care, treatment and support. There is some existing protection against HIV-based discrimination under international law, but the extent of states' obligations to address such discrimination has not been comprehensively addressed in an international instrument.The United Nations Convention on the Rights of Persons with Disabilities entered into force in May 2008. As countries ratify the convention, they are required to amend national laws and policies to give greater protection to the human rights of people with disabilities, including abolishing disability-based discrimination by the state and protecting persons against such discrimination by others. The Disability Convention addresses many of the issues faced by people living with HIV (PLHIV) but does not explicitly include HIV or AIDS within its open-ended definition of "disability".Therefore, the advent of the Disability Convention prompts us to consider the links between HIV and disability and, specifically, to consider the opportunities it and other legal mechanisms, international or domestic, may afford for advancing the human rights of PLHIV facing human rights infringements. We do so in the belief that the movement for human rights is stronger when constituencies with so many common and overlapping interests are united, and that respectful and strategic collaboration ultimately strengthens both the disability rights and the AIDS movements.In this article, we first examine the links between HIV and disability. We then provide a brief overview of how international human rights law has treated both disability and HIV/AIDS. We note some of the different ways in which national anti-discrimination laws have reflected the links between HIV and disability, illustrated with representative examples from a number of countries

Full Text Available Abstract Stigma and discrimination constitute one of the greatest barriers to dealing effectively with the HIV epidemic, underlying a range of human rights violations and hindering access to prevention, care, treatment and support. There is some existing protection against HIV-based discrimination under international law, but the extent of states' obligations to address such discrimination has not been comprehensively addressed in an international instrument. The United Nations Convention on the Rights of Persons with Disabilities entered into force in May 2008. As countries ratify the convention, they are required to amend national laws and policies to give greater protection to the human rights of people with disabilities, including abolishing disability-based discrimination by the state and protecting persons against such discrimination by others. The Disability Convention addresses many of the issues faced by people living with HIV (PLHIV but does not explicitly include HIV or AIDS within its open-ended definition of "disability". Therefore, the advent of the Disability Convention prompts us to consider the links between HIV and disability and, specifically, to consider the opportunities it and other legal mechanisms, international or domestic, may afford for advancing the human rights of PLHIV facing human rights infringements. We do so in the belief that the movement for human rights is stronger when constituencies with so many common and overlapping interests are united, and that respectful and strategic collaboration ultimately strengthens both the disability rights and the AIDS movements. In this article, we first examine the links between HIV and disability. We then provide a brief overview of how international human rights law has treated both disability and HIV/AIDS. We note some of the different ways in which national anti-discrimination laws have reflected the links between HIV and disability, illustrated with representative

Full Text Available The abilities of animals and humans to extract rules from sound sequences have previously been compared using observation of spontaneous responses and conditioning techniques. However, the results were inconsistently interpreted across studies possibly due to methodological and/or species differences. Therefore, we examined the strategies for discrimination of sound sequences in Bengalese finches and humans using the same protocol. Birds were trained on a GO/NOGO task to discriminate between two categories of sound stimulus generated based on an AAB or ABB rule. The sound elements used were taken from a variety of male (M and female (F calls, such that the sequences could be represented as MMF and MFF. In test sessions, FFM and FMM sequences, which were never presented in the training sessions but conformed to the rule, were presented as probe stimuli. The results suggested two discriminative strategies were being applied: 1 memorizing sound patterns of either GO or NOGO stimuli and generating the appropriate responses for only those sounds; and 2 using the repeated element as a cue. There was no evidence that the birds successfully extracted the abstract rule (i.e. AAB and ABB; MMF-GO subjects did not produce a GO response for FFM and vice versa. Next we examined whether those strategies were also applicable for human participants on the same task. The results and questionnaires revealed that participants extracted the abstract rule, and most of them employed it to discriminate the sequences. This strategy was never observed in bird subjects, although some participants used strategies similar to the birds when responding to the probe stimuli. Our results showed that the human participants applied the abstract rule in the task even without instruction but Bengalese finches did not, thereby reconfirming that humans have to extract abstract rules from sound sequences that is distinct from non-human animals.

Studies of cognitive ability in farm animals are valuable, not only because they provide indicators of the commonality of comparative influence, but understanding farm animal cognition may also aid in management and treatment procedures. Here, eight dwarf goats (Capra hircus) learned a series of 10 visual four-choice discriminations using an automated device that allowed individual ad lib. access to the test setup while staying in a familiar environment and normal social setting. The animals were trained on each problem for 5 days, followed by concurrent testing of the current against the previous problem. Once all 10 problems had been learned, they were tested concurrently over the course of 9 days. In initial training, all goats achieved criterion learning levels on nearly all problems within 2 days and under 200 trials. Concurrently presenting the problems trained in adjacent sessions did not impair performance on either problem relative to single-problem learning. Upon concurrent presentation of all 10 previously learned problems, at least half were well-remembered immediately. Although this test revealed a recency effect (later problems were better remembered), many early-learned problems were also well-retained, and 10-item relearning was quite quick. These results show that dwarf goats can retain multiple-problem information proficiently and can do so over periods of several weeks. From an ecological point of view, the ability to form numerous associations between visual cues offered by specific plants and food quality is an important pre-grazing mechanism that helps goats exploit variation in vegetation and graze selectively.

Full Text Available Monolingual infants start learning the prosodic properties of their native language around 6 to 9 months of age, a fact marked by the development of preferences for predominant prosodic patterns and a decrease in sensitivity to non-native prosodic properties. The present study evaluates the effects of bilingual acquisition on speech perception by exploring how stress pattern perception may differ in French-learning 10-month-olds raised in bilingual as opposed to monolingual environments. Experiment 1 shows that monolinguals can discriminate stress patterns following a long familiarization to one of two patterns, but not after a short familiarization. In Experiment 2, two subgroups of bilingual infants growing up learning both French and another language (varying across infants in which stress is used lexically were tested under the more difficult short familiarization condition: one with balanced input, and one receiving more input in the language other than French. Discrimination was clearly found for the other-language-dominant subgroup, establishing heightened sensitivity to stress pattern contrasts in these bilinguals as compared to monolinguals. However, the balanced bilinguals' performance was not better than that of monolinguals, establishing an effect of the relative balance of the language input. This pattern of results is compatible with the proposal that sensitivity to prosodic contrasts is maintained or enhanced in a bilingual population compared to a monolingual population in which these contrasts are non-native, provided that this dimension is used in one of the two languages in acquisition, and that infants receive enough input from that language.

In this study, we explored the time course of haptic stiffness discriminationlearning and how it was affected by two experimental factors, the addition of visual information and/or knowledge of results (KR) during training. Stiffness perception may integrate both haptic and visual modalities. However, in many tasks, the visual field is typically occluded, forcing stiffness perception to be dependent exclusively on haptic information. No studies to date addressed the time course of haptic stiffness perceptual learning. Using a virtual environment (VE) haptic interface and a two-alternative forced-choice discrimination task, the haptic stiffness discrimination ability of 48 participants was tested across 2 days. Each day included two haptic test blocks separated by a training block Additional visual information and/or KR were manipulated between participants during training blocks. Practice repetitions alone induced significant improvement in haptic stiffness discrimination. Between days, accuracy was slightly improved, but decision time performance was deteriorated. The addition of visual information and/or KR had only temporary effects on decision time, without affecting the time course of haptic discriminationlearning. Learning in haptic stiffness discrimination appears to evolve through at least two distinctive phases: A single training session resulted in both immediate and latent learning. This learning was not affected by the training manipulations inspected. Training skills in VE in spaced sessions can be beneficial for tasks in which haptic perception is critical, such as surgery procedures, when the visual field is occluded. However, training protocols for such tasks should account for low impact of multisensory information and KR.

The human visual system seems to have a highly perceptual sensitivity to symmetry. However, where and when the discrimination of symmetrical properties begins in the context of visual information processing is largely unclear. This study investigates event-related potential (ERP) patterns in humans when perceiving symmetry-varied complex object images. ERP responses were derived from electroencephalography (EEG) data recorded from eight healthy subjects using 128-channel scalp electrodes. Visual stimulation was provided using gray-scaled photographs of a car with six different viewpoints, hence disrupting the vertical symmetry, where one of the stimuli was intentionally made symmetric by mirroring the image about its center vertical axis. The results show that discrimination of image symmetry is revealed by potential deflection in early ERP components recorded at occipito-temporal sites and can be significantly observed around 220 ms after stimulus onset.

Changes in synaptic efficacy underlying learning and memory processes are assumed to be associated with alterations of the protein composition of synapses. Here, we performed a quantitative proteomic screen to monitor changes in the synaptic proteome of four brain areas (auditory cortex, frontal cortex, hippocampus striatum) during auditory learning. Mice were trained in a shuttle box GO/NO-GO paradigm to discriminate between rising and falling frequency modulated tones to avoid mild electric foot shock. Control-treated mice received corresponding numbers of either the tones or the foot shocks. Six hours and 24 h later, the composition of a fraction enriched in synaptic cytomatrix-associated proteins was compared to that obtained from naïve mice by quantitative mass spectrometry. In the synaptic protein fraction obtained from trained mice, the average percentage (±SEM) of downregulated proteins (59.9 ± 0.5%) exceeded that of upregulated proteins (23.5 ± 0.8%) in the brain regions studied. This effect was significantly smaller in foot shock (42.7 ± 0.6% down, 40.7 ± 1.0% up) and tone controls (43.9 ± 1.0% down, 39.7 ± 0.9% up). These data suggest that learning processes initially induce removal and/or degradation of proteins from presynaptic and postsynaptic cytoskeletal matrices before these structures can acquire a new, postlearning organisation. In silico analysis points to a general role of insulin-like signalling in this process.

Social learning is a fundamental element of human cognition. Learning from others facilitates the transmission of information that helps individuals and groups rapidly adjust to new environments and underlies adaptive cultural evolution1-6. While basic human propensities for social learning are traditionally assumed to be species-universal1,7, recent empirical studies show that they vary between individuals and populations8-13. Yet the causes of this variation remain poorly understood9. Here we show that interdependence in everyday social and economic activities can strongly amplify social learning. With an experimental decision-making task we examine individual versus social learning in three recently diverged populations of a single-ethnic group, whose subsistence styles require varying degrees of interdependence. Interdependent pastoralists and urban dwellers have markedly higher propensities for social learning than independent horticulturalists, who predominantly rely on individual payoff information. These results indicate that everyday social and economic practices can mould human social learning strategies and they highlight the flexibility of human cognition to change with local ecology. Our study further suggests that shifts in subsistence styles - which can occur when humans inhabit new habitats or cultural niches2 - can alter reliance on social learning and may therefore impact the ability of human societies to adapt to novel circumstances.

The aim of this study was to delineate the minimal conditions for extinction of Pavlovian modulation in humans. Previous experiments at our lab showed that, after X-- A+/A- acquisition training, X- trials did not extinguish differential X-- A+/A- responding, while X-- A- trials did. Additionally, X-- A- extinction training seemed only to extinguish differential X-- A+/A- responding, while leaving differential responding on a concurrently trained Y [Symbol: see text] B+/B- discrimination intact. It thus seemed that the X-- A+/A- discrimination can only be extinguished by X-- A- extinction trials. (Rescorla, Journal of Experimental Psychology: Animal Behavior Processes 12, 16-24, 1986), on the other hand, found that the minimal conditions for extinction were broader in pigeons: Namely, he found that an acquired X-- A+/A- discrimination could be extinguished by presenting the original feature X in combination with a different target (B) that was minimally trained as an exciter. We thus wanted to examine whether this was also the case in humans. We found that nonreinforced X-- B- presentations did not abolish discriminative X-- A/A responding when target B was a nonreinforced stimulus. Nonreinforced X-- B- trials did extinguish the X-- A+/A- discrimination when target B had previously been trained as a target for modulation (X-- B+/B- or Y [Symbol: see text] B+/B- training) or as a reinforced exciter (B+). Our results thusf parallel and extend those in nonhuman animals (Rescorla, Journal of Experimental Psychology: Animal Behavior Processes 12, 16-24, 1986).

The detection of voluntary motor intention from EEG has been applied to closed-loop brain-computer interfacing (BCI). The movement-related cortical potential (MRCP) is a low frequency component of the EEG signal, which represents movement intention, preparation, and execution. In this study, we aim at detecting MRCPs from single-trial EEG traces. For this purpose, we propose a detector based on a discriminant manifold learning method, called locality sensitive discriminant analysis (LSDA), and we test it in both online and offline experiments with executed and imagined movements. The online and offline experimental results demonstrated that the proposed LSDA approach for MRCP detection outperformed the Locality Preserving Projection (LPP) approach, which was previously shown to be the most accurate algorithm so far tested for MRCP detection. For example, in the online tests, the performance of LSDA was superior than LPP in terms of a significant reduction in false positives (FP) (passive FP: 1.6 ±0.9/min versus 2.9 ±1.0/min, p = 0.002, active FP: 2.2 ±0.8/min versus 2.7 ±0.6/min , p = 0.03 ), for a similar rate of true positives. In conclusion, the proposed LSDA based MRCP detection method is superior to previous approaches and is promising for developing patient-driven BCI systems for motor function rehabilitation as well as for neuroscience research.

Full Text Available In an effort to understand the paradox between the expansion of inclusion projects for the Roma and their persisting exclusion, this article explores human rights practice in order to grasp the complexity of meanings of inclusion negotiated in this practice. In this way, we scrutinize whether there are limiting factors within the inclusionary discourse itself. Specifically, we analyze the discourse in transnational judicial, political and civil society actors’ reports on violations of human rights against Roma. A strong shared tendency to frame the violations in terms of discrimination can be discerned in the reports, demonstrating a dominant concept in the human rights discourse for Roma. However, a framing analysis of the underlying assumptions of this concept shows that not all three actors offer the same solutions for obtaining non-discrimination, which can partly explain the limited impact of the ostensibly strong and inclusive anti-discrimination discourse. In contrast, the actors do share a negative attribution of responsibility to the nation states, but the effectiveness of this shared discursive claim can be questioned. This article illustrates how inclusion discourses are actually quite complex to grasp and so it substantiates the need for greater critical understanding of such discourses in further research.

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.

Full Text Available We propose a minimum variance unbiased approximation to the conditional relative entropy of the distribution induced by the observed frequency estimates, for multi-classification tasks. Such approximation is an extension of a decomposable scoring criterion, named approximate conditional log-likelihood (aCLL, primarily used for discriminativelearning of augmented Bayesian network classifiers. Our contribution is twofold: (i it addresses multi-classification tasks and not only binary-classification ones; and (ii it covers broader stochastic assumptions than uniform distribution over the parameters. Specifically, we considered a Dirichlet distribution over the parameters, which was experimentally shown to be a very good approximation to CLL. In addition, for Bayesian network classifiers, a closed-form equation is found for the parameters that maximize the scoring criterion.

Pulse shape discrimination (PSD) is a variety of statistical classifier. Fully-­realized statistical classifiers rely on a comprehensive set of tools for designing, building, and implementing. PSD advances rely on improvements to the implemented algorithm. PSD advances can be improved by using conventional statistical classifier or machine learning methods. This paper provides the reader with a glossary of classifier-­building elements and their functions in a fully-­designed and operational classifier framework that can be used to discover opportunities for improving PSD classifier projects. This paper recommends reporting the PSD classifier’s receiver operating characteristic (ROC) curve and its behavior at a gamma rejection rate (GRR) relevant for realistic applications.

Facial expressions represent one of the most salient cues in our environment. They communicate the affective state and intent of an individual and, if interpreted correctly, adaptively influence the behavior of others in return. Processing of such affective stimuli is known to require reciprocal signaling between central viscerosensory brain regions and peripheral-autonomic body systems, culminating in accurate emotion discrimination. Despite emerging links between sleep and affective regulation, the impact of sleep loss on the discrimination of complex social emotions within and between the CNS and PNS remains unknown. Here, we demonstrate in humans that sleep deprivation impairs both viscerosensory brain (anterior insula, anterior cingulate cortex, amygdala) and autonomic-cardiac discrimination of threatening from affiliative facial cues. Moreover, sleep deprivation significantly degrades the normally reciprocal associations between these central and peripheral emotion-signaling systems, most prominent at the level of cardiac-amygdala coupling. In addition, REM sleep physiology across the sleep-rested night significantly predicts the next-day success of emotional discrimination within this viscerosensory network across individuals, suggesting a role for REM sleep in affective brain recalibration. Together, these findings establish that sleep deprivation compromises the faithful signaling of, and the "embodied" reciprocity between, viscerosensory brain and peripheral autonomic body processing of complex social signals. Such impairments hold ecological relevance in professional contexts in which the need for accurate interpretation of social cues is paramount yet insufficient sleep is pervasive.

Full Text Available Goal directed behavior and associated learning processes are tightly linked to neuronal activity in the ventral striatum. Mechanisms that integrate task relevant sensory information into striatal processing during decision making and learning are implicitly assumed in current reinforcementmodels, yet they are still weakly understood. To identify the functional activation of cortico-striatal subpopulations of connections during auditory discriminationlearning, we trained Mongolian gerbils in a two-way active avoidance task in a shuttlebox to discriminate between falling and rising frequency modulated tones with identical spectral properties. We assessed functional coupling by analyzing the field-field coherence between the auditory cortex and the ventral striatum of animals performing the task. During the course of training, we observed a selective increase of functionalcoupling during Go-stimulus presentations. These results suggest that the auditory cortex functionally interacts with the ventral striatum during auditory learning and that the strengthening of these functional connections is selectively goal-directed.

Discriminationlearning behavior and retention of a passive avoidance response were studied in male adult offspring of gestating rats exposed to drinking water containing 2 g/l sodium nitrite, throughout the second half of pregnancy. Both in an auditory and visual discriminationlearning paradigm Na

Behavioral, psychophysiological, and neuropsychological studies have revealed large developmental differences in various learning paradigms where learning from positive and negative feedback is essential. The differences are possibly due to the use of distinct strategies that may be related to spatial working memory and attentional control. In this study, strategies in performing a discriminationlearning task were distinguished in a cross-sectional sample of 302 children from 4 to 14 years of age. The trial-by-trial accuracy data were analyzed with mathematical learning models. The best-fitting model revealed three learning strategies: hypothesis testing, slow abrupt learning, and nonlearning. The proportion of hypothesis-testing children increased with age. Nonlearners were present only in the youngest age group. Feature preferences for the irrelevant dimension had a detrimental effect on performance in the youngest age group. The executive functions spatial working memory and attentional control significantly predicted posterior learning strategy probabilities after controlling for age.

In proactive computing, human activity recognition from image sequences is an active research area. This paper presents a novel approach of human activity recognition based on Linear Discriminant Analysis (LDA) of Independent Component (IC) features from shape information. With extracted features, Hidden Markov Model (HMM) is applied for training and recognition. The recognition performance using LDA of IC features has been compared to other approaches including Principle Component Analysis (PCA), LDA of PC, and ICA. The preliminary results show much improved performance in the recognition rate with our proposed method.

Full Text Available Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to

Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and

In this article, we examine the extent to which children with autism and children with learning difficulties can be discriminated from their responses to different patterns of sensory stimuli. Using an adapted version of the Short Sensory Profile (SSP), sensory processing was compared in 34 children with autism to 33 children with typical…

A total of 48 fifth-grade and 30 nursery-school subjects were administered picture pairs in a discriminationlearning experiment. In addition to a control group, one group of subjects (pre-choice) was instructed to pronounce both items before choosing one, and another group (post-choice) was told to say both items after choosing one. The…

Participants' usage of informational variables in learning visual relative-mass discrimination in collisions was tracked by means of PROBIT correlations. Four groups received feedback that was true or accorded with either of three nonspecificational cue variables. A majority in each group adopted the feedback, but several participants defied the…

Humans are characterized by an extreme dependence on culturally transmitted information. Such dependence requires the complex integration of social and asocial information to generate effective learning and decision making. Recent formal theory predicts that natural selection should favour adaptive learning strategies, but relevant empirical work is scarce and rarely examines multiple strategies or tasks. We tested nine hypotheses derived from theoretical models, running a series of experiments investigating factors affecting when and how humans use social information, and whether such behaviour is adaptive, across several computer-based tasks. The number of demonstrators, consensus among demonstrators, confidence of subjects, task difficulty, number of sessions, cost of asocial learning, subject performance and demonstrator performance all influenced subjects' use of social information, and did so adaptively. Our analysis provides strong support for the hypothesis that human social learning is regulated by adaptive learning rules.

Managing human resource learning for innovation develops a systemic understanding of building innovative capabilities. Building innovative capabilities require active creation, coordination and absorption of useful knowledge and thus a cohesive management approach to learning. Often learning...... in organizations and work is approached without considerations on how to integrate it in the management of human resources. The book investigates the empirical conditions for managing human resources learning for innovation. With focus on innovative performance the importance of modes of innovation, clues...... for organizing learning and ways of utilizing employee knowledge are considered as main challenges. Identification of principles and management instruments are based on research, which means generated form theoretical knowledge and empirical panel data covering firms from the private urban sector in Denmark...

The phenomenon of colour constancy in human visual perception keeps surface colours constant, despite changes in their reflected light due to changing illumination. Although colour constancy has evolved under a constrained subset of illuminations, it is unknown whether its underlying mechanisms, thought to involve multiple components from retina to cortex, are optimised for particular environmental variations. Here we demonstrate a new method for investigating colour constancy using illumination matching in real scenes which, unlike previous methods using surface matching and simulated scenes, allows testing of multiple, real illuminations. We use real scenes consisting of solid familiar or unfamiliar objects against uniform or variegated backgrounds and compare discrimination performance for typical illuminations from the daylight chromaticity locus (approximately blue-yellow) and atypical spectra from an orthogonal locus (approximately red-green, at correlated colour temperature 6700 K), all produced in real time by a 10-channel LED illuminator. We find that discrimination of illumination changes is poorer along the daylight locus than the atypical locus, and is poorest particularly for bluer illumination changes, demonstrating conversely that surface colour constancy is best for blue daylight illuminations. Illumination discrimination is also enhanced, and therefore colour constancy diminished, for uniform backgrounds, irrespective of the object type. These results are not explained by statistical properties of the scene signal changes at the retinal level. We conclude that high-level mechanisms of colour constancy are biased for the blue daylight illuminations and variegated backgrounds to which the human visual system has typically been exposed.

Full Text Available The phenomenon of colour constancy in human visual perception keeps surface colours constant, despite changes in their reflected light due to changing illumination. Although colour constancy has evolved under a constrained subset of illuminations, it is unknown whether its underlying mechanisms, thought to involve multiple components from retina to cortex, are optimised for particular environmental variations. Here we demonstrate a new method for investigating colour constancy using illumination matching in real scenes which, unlike previous methods using surface matching and simulated scenes, allows testing of multiple, real illuminations. We use real scenes consisting of solid familiar or unfamiliar objects against uniform or variegated backgrounds and compare discrimination performance for typical illuminations from the daylight chromaticity locus (approximately blue-yellow and atypical spectra from an orthogonal locus (approximately red-green, at correlated colour temperature 6700 K, all produced in real time by a 10-channel LED illuminator. We find that discrimination of illumination changes is poorer along the daylight locus than the atypical locus, and is poorest particularly for bluer illumination changes, demonstrating conversely that surface colour constancy is best for blue daylight illuminations. Illumination discrimination is also enhanced, and therefore colour constancy diminished, for uniform backgrounds, irrespective of the object type. These results are not explained by statistical properties of the scene signal changes at the retinal level. We conclude that high-level mechanisms of colour constancy are biased for the blue daylight illuminations and variegated backgrounds to which the human visual system has typically been exposed.

The aim of this paper is to identify the common neural signatures based on which the positive and negative valence of human emotions across multiple subjects can be reliably discriminated. The brain activity is observed via Event Related Potentials (ERPs). ERPs are transient components in the Electroencephalography (EEG) generated in response to a stimulus. ERPs were collected while subjects were viewing images with positive or negative emotional content. Building inter-subject discrimination models is a challenging problem due to the high ERPs variability between individuals. We propose to solve this problem with the aid of the Echo State Networks (ESN) as a general framework for extracting the most relevant discriminative features between multiple subjects. The original feature vector is mapped into the reservoir feature space defined by the number of the reservoir equilibrium states. The dominant features are extracted iteratively from low dimensional combinations of reservoir states. The relevance of the new feature space was validated by experiments with standard supervised and unsupervised machine learning techniques. From one side this proof of concept application enhances the usability context of the reservoir computing for high dimensional static data representations by low-dimensional feature transformation as functions of the reservoir states. From other side, the proposed solution for emotion valence detection across subjects is suitable for brain studies as a complement to statistical methods. This problem is important because such decision making systems constitute "virtual sensors" of hidden emotional states, which are useful in psychology science research and clinical applications.

Percutaneous electrical stimulation of the motor point of the first dorsal interosseous muscle (FDI) was used to produce a non-painful contraction of the FDI muscle that caused index finger abduction movement but no radiating cutaneous paraesthesias or sharp sensations localized to joints. Pairs of stimuli separated by different time intervals were given and subjects were asked to report whether they perceived a single or a double index finger abduction movement. The threshold value was the shortest interval for which the subjects reported two separate index finger abduction movements. Temporal discrimination movement thresholds (TDMT) were measured for both right and left hand. To assess the possible role of muscle and cutaneous afferents in temporal discrimination, we investigated the effects of high-frequency (20 Hz) electrical stimulation of the right ulnar and radial nerves on TDMT. In humans, muscle afferents from FDI are supplied by the ulnar nerve whereas the cutaneous territory overlying the muscle and joint is supplied by the radial and median nerves. Threshold values were not significantly different for right (75.1 ms) and left (75.6 ms) hands. During ulnar and to a lesser extent during radial nerve stimulation, TDMT values were significantly increased (119.2 and 93.5 ms, respectively) compared with baseline conditions (78.0 ms) whereas no changes were observed during median nerve stimulation (80.5 ms). These results suggest that muscle, and in part cutaneous, afferents contribute to temporal discrimination of a dual movement. The technique may provide a useful way of measuring temporal discrimination of kinaesthetic inputs in humans.

In animals, two-choice drug discrimination studies have demonstrated that the behavioral effects of 3,4-methylenedioxymethamphetamine (MDMA) are mediated by dopaminergic and serotonergic systems. In order to delineate the relative role of these systems, three-choice paradigms have been used in animals, with findings indicating a more prominent role for serotonin. Human studies assessing the subjective and physiological effects of MDMA have also indicated a mixed action. To parallel animal studies, the participants in the present study were trained to discriminate among a prototypic dopaminergic agonist, d-amphetamine, a prototypic serotonergic agonist, meta-chlorophenylpiperazine (mCPP) and placebo and then were tested with two doses of MDMA. In addition, subjective and physiological effects were measured. The results demonstrated that humans could be trained to discriminate among 20 mg d-amphetamine, 0.75 mg/kg mCPP and placebo. When tested with 1.0 and 1.5 mg/kg, half the participants reported MDMA to be like amphetamine and half like mCPP. There were no clear differences between these two groups in other dimensions, although there was an indication that the individuals who discriminated MDMA as d-amphetamine were more sensitive to the effects of all the drugs. The subjective effects of all three drugs overlapped, although the effects of MDMA appeared more amphetamine-like.

Traditionally, perceptual learning in humans and classical conditioning in animals have been considered as two very different research areas, with separate problems, paradigms, and explanations. However, a number of themes common to these fields of research emerge when they are approached from the more general concept of representational learning. To demonstrate this, I present results of several learning experiments with human adults and infants, exploring how internal representations of complex unknown visual patterns might emerge in the brain. I provide evidence that this learning cannot be captured fully by any simple pairwise associative learning scheme, but rather by a probabilistic inference process called Bayesian model averaging, in which the brain is assumed to formulate the most likely chunking/grouping of its previous experience into independent representational units. Such a generative model attempts to represent the entire world of stimuli with optimal ability to generalize to likely scenes in the future. I review the evidence showing that a similar philosophy and generative scheme of representation has successfully described a wide range of experimental data in the domain of classical conditioning in animals. These convergent findings suggest that statistical theories of representational learning might help to link human perceptual learning and animal classical conditioning results into a coherent framework.

Intranasal insulin delivery is currently being used in clinical trials to test for improvement in human memory and cognition, and in particular, for lessening memory loss attributed to neurodegenerative diseases. Studies have reported the effects of short-term intranasal insulin treatment on various behaviors, but less have examined long-term effects. The olfactory bulb contains the highest density of insulin receptors in conjunction with the highest level of insulin transport within the brain. Previous research from our laboratory has demonstrated that acute insulin intranasal delivery (IND) enhanced both short- and long-term memory as well as increased two-odor discrimination in a two-choice paradigm. Herein, we investigated the behavioral and physiological effects of chronic insulin IND. Adult, male C57BL6/J mice were intranasally treated with 5μg/μl of insulin twice daily for 30 and 60days. Metabolic assessment indicated no change in body weight, caloric intake, or energy expenditure following chronic insulin IND, but an increase in the frequency of meal bouts selectively in the dark cycle. Unlike acute insulin IND, which has been shown to cause enhanced performance in odor habituation/dishabituation and two-odor discrimination tasks in mice, chronic insulin IND did not enhance olfactometry-based odorant discrimination or olfactory reversal learning. In an object memory recognition task, insulin IND-treated mice did not perform differently than controls, regardless of task duration. Biochemical analyses of the olfactory bulb revealed a modest 1.3 fold increase in IR kinase phosphorylation but no significant increase in Kv1.3 phosphorylation. Substrate phosphorylation of IR kinase downstream effectors (MAPK/ERK and Akt signaling) proved to be highly variable. These data indicate that chronic administration of insulin IND in mice fails to enhance olfactory ability, object memory recognition, or a majority of systems physiology metabolic factors - as reported to

As an effective technique to identify counterfeit drugs, Near Infrared Spectroscopy has been successfully used in the drug management of grass-roots units, with classifier modeling of Pattern Recognition. Due to a major disadvantage of the characteristic overlap and complexity, the wide bandwidth and the weak absorption of the Spectroscopy signals, it seems difficult to give a satisfactory solutions for the modeling problem. To address those problems, in the present paper, a summation wavelet extreme learning machine algorithm (SWELM(CS)) combined with Cuckoo research was adopted for drug discrimination by NIRS. Specifically, Extreme Learning Machine (ELM) was selected as the classifier model because of its properties of fast learning and insensitivity, to improve the accuracy and generalization performances of the classifier model; An inverse hyperbolic sine and a Morlet-wavelet are used as dual activation functions to improve convergence speed, and a combination of activation functions makes the network more adequate to deal with dynamic systems; Due to ELM' s weights and hidden layer threshold generated randomly, it leads to network instability, so Cuckoo Search was adapted to optimize model parameters; SWELM(CS) improves stability of the classifier model. Besides, SWELM(CS) is based on the ELM algorithm for fast learning and insensitivity; the dual activation functions and proper choice of activation functions enhances the capability of the network to face low and high frequency signals simultaneously; it has high stability of classification by Cuckoo Research. This compact structure of the dual activation functions constitutes a kernel framework by extracting signal features and signal simultaneously, which can be generalized to other machine learning fields to obtain a good accuracy and generalization performances. Drug samples of near in- frared spectroscopy produced by Xian-Janssen Pharmaceutical Ltd were adopted as the main objects in this paper

Pavlovian conditioning underlies many aspects of pain behavior, including fear and threat detection [1], escape and avoidance learning [2], and endogenous analgesia [3]. Although a central role for the amygdala is well established [4], both human and animal studies implicate other brain regions in learning, notably ventral striatum and cerebellum [5]. It remains unclear whether these regions make different contributions to a single aversive learning process or represent independent learning mechanisms that interact to generate the expression of pain-related behavior. We designed a human parallel aversive conditioning paradigm in which different Pavlovian visual cues probabilistically predicted thermal pain primarily to either the left or right arm and studied the acquisition of conditioned Pavlovian responses using combined physiological recordings and fMRI. Using computational modeling based on reinforcement learning theory, we found that conditioning involves two distinct types of learning process. First, a non-specific "preparatory" system learns aversive facial expressions and autonomic responses such as skin conductance. The associated learning signals-the learned associability and prediction error-were correlated with fMRI brain responses in amygdala-striatal regions, corresponding to the classic aversive (fear) learning circuit. Second, a specific lateralized system learns "consummatory" limb-withdrawal responses, detectable with electromyography of the arm to which pain is predicted. Its related learned associability was correlated with responses in ipsilateral cerebellar cortex, suggesting a novel computational role for the cerebellum in pain. In conclusion, our results show that the overall phenotype of conditioned pain behavior depends on two dissociable reinforcement learning circuits.

Two Japanese students were taught to pronounce and discriminate English words that contain unfamiliar phonemic contrasts (e.g., rock and lock). Teaching pronunciation was found to be easier than teaching listening discrimination. Teaching listening discrimination resulted in collateral improvement in pronunciation and, to a lesser extent, vice versa.

Two Japanese students were taught to pronounce and discriminate English words that contain unfamiliar phonemic contrasts (e.g., rock and lock). Teaching pronunciation was found to be easier than teaching listening discrimination. Teaching listening discrimination resulted in collateral improvement in pronunciation and, to a lesser extent, vice versa.

Full Text Available Nanonetwork design and analysis has become a very interesting topic in recent years. Though this area of research is in its formative stage, it definitely posses a strong integrity in finding out numerous applications in medical and allied sciences. Nanonetworking is indeed a nature built foundation which comprises human intra body communications. Somatosensory system is the one of the critical and must have systems of human body. This literature concentrates on the body discriminative touch and proprioception mechanism of somatosensory system. This particular system is well architecture by medial lemniscal pathway, in human body for transduction of touch and proprioceptive information. This paper seeks out the novel communication channel model of somatosensory system. The working principle of the channel model is established by an equivalent Moore machine. A novel algorithm MLP is proposed after its name, medial lemniscal pathway. A novel naomachine and appropriate processing unit are also devised, based on the automaton.

The color discrimination is a powerful tool for detection of eye diseases, and it is is necessary to produce different kinds of color rapidly and precisely for testing color discrimination thresholds of human eyes. Three channels' pulse-width modulation (PWM) and light-mixing technology is a new way to mixing color, and a new measurement method for color discrimination thresholds of human eyes based on PWM light-mix technology can generate kinds of color stimuli. In this study, 5 youth volunteers were measured via this equipment after the test for the stability of the device's illumination and chrominance. Though the theory of Macadam ellipses and the interleaved staircase method, a psychophysical experiment was made to study the color discrimination threshold of the human eyes around a basic color center. By analyzing the data of the chromatic ellipse and the color discrimination threshold, the result shows that each color is not uniform in a single color region and the color difference threshold of normal human is around the third Macadam ellipses. The experimental results show that the repeatability and accuracy of the observer can meet the accuracy requirements of the relevant experiments, and the data is reliable and effective, which means the measurement method is an effective way to measure the color discrimination thresholds of human visual system.

Full Text Available Corvids and primates have been shown to possess similar cognitive adaptations, yet these animals are seldom tested using similar procedures. Object-choice tasks, which have commonly been used to test whether an animal is able to infer the mental state of a human experimenter based on a gestural cue, provide one potential means of testing these animals using a similar paradigm. The current study used an object-choice task to examine whether the corvid, Clark's nutcracker (Nucifraga columbiana, is able to use a cognitive strategy to discriminate between the knowledge states of two human experimenters. One experimenter was informed, and the other uninformed, as to the location of a food reward hidden inside one of two opaque containers. During the Uninformed Gesture condition, the nutcrackers were given probe tests during which only the person performing as the uninformed experimenter provided a gesture. Thus, the nutcrackers could not use the experimenter's gesture to reliably find the food. During the Gesture Conflict condition, the nutcrackers were presented with a cue conflict. During probe tests, both the informed and the uninformed experimenter gestured to separate containers. Thus, to find the food the nutcrackers had to use the gesture from the informed experimenter and refrain from using the gesture of the uninformed experimenter. Our results showed that when the uninformed experimenter's gesture was presented alone, the birds continued to follow the gesture even though it was not consistently predictive of the food's location. However, when provided with two conflicting gestures, as a group the nutcrackers responded to the gesture of the informed experimenter at above chance levels. These results suggest that the birds had learned that the gesture was informative, perhaps by associative learning, yet when this mechanism was not reliable the nutcrackers were able to use either the human experimenters' presence/absence during the baiting

This paper describes the estimation of the optimal measurement position by discriminant analysis based on Wilks' lambda for myoelectric hand control. In previous studies, for motion discrimination, the myoelectric signals were measured at the same positions. However, the optimal measurement positions of the myoelectric signals for motion discrimination differ depending on the remaining muscles of amputees. Therefore, the purpose of this study is to estimate the optimal and fewer measurement positions for precise motion discrimination of a human forearm. This study proposes a method for estimating the optimal measurement positions by discriminant analysis based on Wilks' lambda, using the myoelectric signals measured at multiple positions. The results of some experiments on the myoelectric hand simulator show the effectiveness of the proposed optimal measurement position estimation method.

The performance of 23 patients with moderate-severe traumatic brain injury on the California Verbal Learning Test, Second Edition (CVLT-II; Delis et al., 2000) was compared with that of 23 matched healthy controls to determine whether recall discriminability indices, which take into account both correct target recall and intrusive errors, would provide better diagnostic classification than traditional variables that are based exclusively on correct recall. Patients with traumatic brain injury recalled fewer correct words, and also made more intrusive errors, on CVLT-II short and long delay, free and cued recall trials (p recall discriminability indices yielded a classification of clinical versus control participants (72%) that was not significantly different from one based on traditional variables (74%). We conclude that CVLT-II recall discriminability indices do not routinely provide an advantage over traditional variables in patients with traumatic brain injury.

There is growing interest in the potential of neuroimaging to help develop non-invasive biomarkers in neurodegenerative diseases. In this study, connection-wise patterns of functional connectivity were used to distinguish Parkinson’s disease patients according to cognitive status using machine learning. Two independent subject samples were assessed with resting-state fMRI. The first (training) sample comprised 38 healthy controls and 70 Parkinson’s disease patients (27 with mild cognitive impairment). The second (validation) sample included 25 patients (8 with mild cognitive impairment). The Brainnetome atlas was used to reconstruct the functional connectomes. Using a support vector machine trained on features selected through randomized logistic regression with leave-one-out cross-validation, a mean accuracy of 82.6% (p discriminating Parkinson’s disease patients according to the presence of cognitive deficits. PMID:28349948

Partner choice on the basis of an individual's reliability is expected to stabilize social interactions. In this experiment, we tested whether carrion crows (Corvus corone corone) learn to differentiate between calls of reliable or unreliable individuals. Crows were kept in an aviary that comprised four visually but not acoustically isolated compartments, separated by a central room. In an association phase, a dead crow placed in the central compartment was visible only to one of the four crow groups, whilst alert calls of a conspecific were played back. Therefore, these calls were reliable for that group, but unreliable for the three other groups. The procedure was repeated, using a different reliable caller for each group. In two test sessions, 1 month apart, reliable and unreliable model individuals were played back, but no dead crow was presented. We quantified birds' attention behaviour and the number of vocalisations emitted. In the association phase, crows were more attentive towards the reliable compared with the unreliable stimuli and called more in response to reliable compared to unreliable individuals. In the test and repeat phase, attention behaviour did not differ between reliability conditions, but the pattern of vocal behaviour reversed, with crows calling less frequent when listening to reliable compared with unreliable calls. Vocal responses of crows suggest that they can discriminate between reliable and unreliable callers.

Blood, saliva, semen and vaginal secretions are the main human body fluids encountered at crime scenes. Currently presumptive tests are routinely utilised to indicate the presence of body fluids, although these are often subject to false positives and limited to particular body fluids. Over the last decade more sensitive and specific body fluid identification methods have been explored, such as mRNA analysis and proteomics, although these are not yet appropriate for routine application. This research investigated the application of ATR FT-IR spectroscopy for the detection and discrimination of human blood, saliva, semen and vaginal secretions. The results demonstrated that ATR FT-IR spectroscopy can detect and distinguish between these body fluids based on the unique spectral pattern, combination of peaks and peak frequencies corresponding to the macromolecule groups common within biological material. Comparisons with known abundant proteins relevant to each body fluid were also analysed to enable specific peaks to be attributed to the relevant protein components, which further reinforced the discrimination and identification of each body fluid. Overall, this preliminary research has demonstrated the potential for ATR FT-IR spectroscopy to be utilised in the routine confirmatory screening of biological evidence due to its quick and robust application within forensic science.

Learning is known to facilitate our ability to detect targets in clutter and optimize brain processes for successful visual recognition. Previous brain-imaging studies have focused on identifying spatial patterns (i.e., brain areas) that change with learning, implicating occipitotemporal and frontoparietal areas. However, little is known about the interactions within this network that mediate learning-dependent improvement in complex perceptual tasks (i.e., discrimination of visual forms in clutter). Here we take advantage of the complementary high spatial and temporal resolution of simultaneous EEG-fMRI to identify the learning-dependent changes in spatiotemporal brain patterns that mediate enhanced behavioral sensitivity in the discrimination of global forms after training. We measured the observers' choices when discriminating between concentric and radial patterns presented in noise before and after training. Similarly, we measured the choices of a pattern classifier when predicting each stimulus from EEG-fMRI signals. By comparing the performance of human observers and classifiers, we demonstrated that learning alters sensitivity to visual forms and EEG-fMRI activation patterns related to distinct visual recognition processes. In particular, behavioral improvement after training was associated with changes in (1) early processes involved in the integration of global forms in higher occipitotemporal and parietal areas, and (2) later processes related to categorical judgments in frontal circuits. Thus, our findings provide evidence that learning acts on distinct visual recognition processes and shapes feedforward interactions across brain areas to support performance in complex perceptual tasks.

The past decade has seen a dramatic change in our understanding of the role of the striatum in behavior. Early perspectives emphasized a role for the striatum in habitual learning of stimulus-response associations and sequences of actions. Recent advances from human neuroimaging research suggest a broader role for the striatum in motivated learning. New findings demonstrate that the striatum represents multiple learning signals and highlight the contribution of the striatum across many cognitive domains and contexts. Recent findings also emphasize interactions between the striatum and other specialized brain systems for learning. Together, these findings suggest that the striatum contributes to a distributed network that learns to select actions based on their predicted value in order to optimize behavior.

Experiments used an affective priming procedure to investigate whether evaluative conditioning in humans is subject to bias as a consequence of differences in the learned predictiveness of the cues involved. Experiment 1, using brief prime presentation, demonstrated stronger affective priming for

Experiments used an affective priming procedure to investigate whether evaluative conditioning in humans is subject to bias as a consequence of differences in the learned predictiveness of the cues involved. Experiment 1, using brief prime presentation, demonstrated stronger affective priming for cu

Fear conditioning is considered an animal model of post-traumatic stress disorder. Such models have shown fear conditioning disrupts subsequent rapid eye movement sleep (REM). Here, we provide a translation of these models into humans. Using the fear potentiated startle (FPS) procedure, we examined the effects of fear conditioning and safety signal learning on subsequent REM sleep in healthy adults. We also examined the effects of changes in REM sleep on retention of fear and safety learning. Participants (n = 42 normal controls) spent 3 consecutive nights in the laboratory. The first was an adaptation night. Following the second night, we administered a FPS procedure that included pairing a wrist shock with a threat signal and a safety signal never paired with a shock. The next day, we administered the FPS procedure again, with no wrist shocks to any stimulus, to measure retention of fear and safety. Canonical correlations assessed the relationship between FPS response and REM sleep. Results demonstrated that increased safety signal learning during the initial acquisition phase was associated with increased REM sleep consolidation that night, with 28.4% of the variance in increased REM sleep consolidation from baseline accounted for by safety signal learning. Overnight REM sleep was, in turn, related to overnight retention of fear and safety learning, with 22.5% of the variance in startle retention accounted for by REM sleep. These data suggest that sleep difficulties, specifically REM sleep fragmentation, may play a mechanistic role in post-traumatic stress disorder via an influence on safety signal learning and/or threat-safety discrimination.

The sense of taste helps humans to obtain information and form a picture of the world by recognizing chemicals in their environments. Over the past decade, large advances have been made in understanding the mechanisms of taste detection and mimicking its capability using artificial sensor devices. However, the detection capability of previous artificial taste sensors has been far inferior to that of animal tongues, in terms of its sensitivity and selectivity. Herein, we developed a bioelectronic tongue using heterodimeric human sweet taste receptors for the detection and discrimination of sweeteners with human-like performance, where single-walled carbon nanotube field-effect transistors were functionalized with nanovesicles containing human sweet taste receptors and used to detect the binding of sweeteners to the taste receptors. The receptors are heterodimeric G-protein-coupled receptors (GPCRs) composed of human taste receptor type 1 member 2 (hTAS1R2) and human taste receptor type 1 member 3 (hTAS1R3), which have multiple binding sites and allow a human tongue-like broad selectivity for the detection of sweeteners. This nanovesicle-based bioelectronic tongue can be a powerful tool for the detection of sweeteners as an alternative to labor-intensive and time-consuming cell-based assays and the sensory evaluation panels used in the food and beverage industry. Furthermore, this study also allows the artificial sensor to exam the functional activity of dimeric GPCRs.

The bag-of-features model is a distinctive and robust approach to detect human actions in videos. The discriminative power of this model relies heavily on the quantization of the video features into visual words. The quantization determines how well the visual words describe the human action. Random

The bag-of-features model is a distinctive and robust approach to detect human actions in videos. The discriminative power of this model relies heavily on the quantization of the video features into visual words. The quantization determines how well the visual words describe the human action. Random

This paper discusses the applicability of the Wavelet transform for analyzing an EMG signal and discriminating motion classes. In many previous works, researchers have dealt with steady EMG and have proposed suitable analyzing methods for the EMG, for example FFT and STFT. Therefore, it is difficult for the previous approaches to discriminate motions from the EMG in the different phases of muscle activity, i.e., pre-activity, in activity, postactivity phases, as well as the period of motion transition from one to another. In this paper, we introduce the Wavelet transform using the Coiflet mother wavelet into our real-time EMG prosthetic hand controller for discriminating motions from steady and unsteady EMG. A preliminary experiment to discriminate three hand motions from four channel EMG in the initial pre-activity and in activity phase is carried out to show the effectiveness of the approach. However, future research efforts are necessary to discriminate more motions much precisely.

A 2-layer symbolic network model based on the equilibrium equations of the Rescorla-Wagner model (Danks, 2003) is proposed. The study first presents 2 experiments in Serbian, which reveal for sentential reading the inflectional paradigmatic effects previously observed by Milin, Filipović Đurđević, and Moscoso del Prado Martín (2009) for unprimed lexical decision. The empirical results are successfully modeled without having to assume separate representations for inflections or data structures such as inflectional paradigms. In the next step, the same naive discriminativelearning approach is pitted against a wide range of effects documented in the morphological processing literature. Frequency effects for complex words as well as for phrases (Arnon & Snider, 2010) emerge in the model without the presence of whole-word or whole-phrase representations. Family size effects (Moscoso del Prado Martín, Bertram, Häikiö, Schreuder, & Baayen, 2004; Schreuder & Baayen, 1997) emerge in the simulations across simple words, derived words, and compounds, without derived words or compounds being represented as such. It is shown that for pseudo-derived words no special morpho-orthographic segmentation mechanism, as posited by Rastle, Davis, and New (2004), is required. The model also replicates the finding of Plag and Baayen (2009) that, on average, words with more productive affixes elicit longer response latencies; at the same time, it predicts that productive affixes afford faster response latencies for new words. English phrasal paradigmatic effects modulating isolated word reading are reported and modeled, showing that the paradigmatic effects characterizing Serbian case inflection have crosslinguistic scope.

Full Text Available To disentangle taste from reward responses in the human gustatory cortex, we combined high density electro-encephalography with a gustometer delivering tastant puffs to the tip of the tongue. Stimuli were pure tastants (salt solutions at two concentrations, caloric emulsions of identical taste (two milk preparations differing in fat content and a mixture of high fat milk with the lowest salt concentration. Early event-related potentials showed a dose-response effect for increased taste intensity, with higher amplitude and shorter latency for high compared to low salt concentration, but not for increased fat content. However, the amplitude and distribution of late potentials were modulated by fat content independently of reported intensity and discrimination. Neural source estimation revealed a sustained activation of reward areas to the two high-fat stimuli. The results suggest calorie detection through specific sensors on the tongue independent of perceived taste. Finally, amplitude variation of the first peak in the event-related potential to the different stimuli correlated with papilla density, suggesting a higher discrimination power for subjects with more fungiform papillae.

Full Text Available Decisions made by individuals can be influenced by what others think and do. Social learning includes a wide array of behaviors such as imitation, observational learning of novel foraging techniques, peer or parental influences on individual preferences, as well as outright teaching. These processes are believed to underlie an important part of cultural variation among human populations and may also explain intraspecific variation in behavior between geographically distinct populations of animals. Recent neurobiological studies have begun to uncover the neural basis of social learning. Here we review experimental evidence from the past few decades showing that social learning is a widespread set of skills present in multiple animal species. In mammals, the temporoparietal junction, the dorsomedial and dorsolateral prefrontal cortex, as well as the anterior cingulate gyrus, appear to play critical roles in social learning. Birds, fish and insects also learn from others, but the underlying neural mechanisms remain poorly understood. We discuss the evolutionary implications of these findings and highlight the importance of emerging animal models that permit precise modification of neural circuit function for elucidating the neural basis of social learning.

Pigeons learned a series of reversals of a simultaneous red-green discrimination with a 6-s delay of reinforcement. The signal properties during the 6-s reinforcement delay were varied across blocks of reversals, such that the delay was either unsignaled (intertrial interval conditions during the delay) or signaled by illumination of the center key. Four different signal conditions were presented: (1) signals only after S+ responses, (2) signals only after S- responses, (3) differential signals after S+ versus S- responding, and (4) the same nondifferential signals after S+ and S- responses. (A zero-delay control condition was also included.) Learning was at a high level in the S+ -only and differential-signal conditions, and learning was at a low level during the unsignaled, nondifferentially signaled, and S- signal conditions. Thus, a differential stimulus contingent on correct choices was necessary for proficient learning-to-learn, even though within-reversal learning occurred in all conditions. During the S+ and differential-signal conditions, improvement in learning continued to occur even after more than 240 reversals (more than 38,000 trials).

Full Text Available Global companies are pressed by the need to simultaneously manage globally since they consider the whole world as their own market, and locally, because the global market consists of various different and weakly connected market segments. The need to be global and local at the same time presents, perhaps the most important challenge for management of global companies in 21st century. Searching this balance presents also an important challenge for human resource management (HRM, regarding the ways of accomplishing it. HRM is expected to contribute to achievement of global competitive advantage worldwide efficiency, local responsiveness, as well as transfer of learning within global organizations. The transfer of learning gains on its importance as many authors see it as the main motive of establishing global companies. However, regardless of recognized significance of organizational learning for global companies, international HRM literature simply lacks studies related to transfer of learning, recommendations about how to develop this organizational ability, how to improve it and measure, and how to provide permanency of the learning process. Therefore, the aim of this paper is through reviewing the relevant literature, to shed light on different aspects of the responsiveness-integration paradigm and its implications on the transfer of learning in global companies.

Full Text Available ENGLISH: The Human Rights Advisory Panel (HRAP established in 2006 to strengthen the accountability of UNMIK in Kosovo so far has dealt mainly with cases regarding property and missing persons. In two recent cases of members of the Egyptian and the Serbian minority (Fillim Guga and Nevenka Ristić it also dealt with privatization of socially - owned enterprises and found discrimination on ethnic grounds by the Special Chamber of the Supreme Court, established by UNMIK for such cases, which raises the accountability of UNMIK. In doing so the panel applied Article 14 of the ECHR on prohibition of discrimination in conjunction with Article 6 ECHR on fair trial in the light of relevant jurisprudence of the European Court of Human Rights. It also pointed out that in these cases the Special Chamber did not recognize a prima facie case of indirect discrimination and did not apply the principle of reversal of proof as required by the Anti - Discrimination Law of Kosovo. On behalf of UNMIK, the Special Representative of the Secretary - General defended the findings of the Special Chamber. The conclusions and recommendations in the Opinion of the Panel hold UNMIK accountable for the violations found and require it to take immediate and effective measures including an apology and adequate compensation for non-pecuniary damage as well as urging EULEX and other competent authorities in Kosovo to reopen the case by the Special Chamber. The work of the HRAP raises wider issues of accountability of international missions like UNMIK, to which it makes an important contribution. DEUTSCH: Das menschenrechtliche Beratungspanel, welches 2006 ins Leben gerufen wurde, um die Verantwortlichkeit von UNMIK im Kosovo zu stärken, hat sich bisher hauptsächlich mit Fällen zum Eigentumsrecht und hinsichtlich verschwundener Personen beschäftigt. In zwei aktuellen Fällen, die Mitglieder der ägyptischen bzw. serbischen Minderheit betrafen (Fillim Guga und Nevenka Risti

Auditory P50 suppression, which is assessed using a paired auditory stimuli (S1 and S2) paradigm to record the P50 mid-latency evoked potential, is assumed to reflect sensory gating. Recently, P50 suppression deficits were observed in patients with anxiety disorders, including panic disorder, post-traumatic stress disorder and obsessive-compulsive disorder, as we previously reported. The processes of fear conditioning are thought to play a role in the pathophysiology of anxiety disorders. In addition, we found that the P50 sensory gating mechanism might be physiologically associated with fear conditioning and extinction in a simple human fear-conditioning paradigm that involved a light signal as a conditioned stimulus (CS+). Our objective was to investigate the different patterns of P50 suppression in a discrimination fear-conditioning paradigm with both a CS+ (danger signal) and a CS- (safety signal). Twenty healthy volunteers were recruited. We measured the auditory P50 suppression in the control (baseline) phase, in the fear-acquisition phase, and in the fear-extinction phase using a discrimination fear-conditioning paradigm. Two-way (CSs vs. phase) Analysis of variance with repeated measures demonstrated a significant interaction between the two factors. Post-hoc LSD analysis indicated that the P50 S2/S1 ratio in the CS+ acquisition phase was significantly higher than that in the CS- acquisition phase. These results suggest that the auditory P50 sensory gating might differ according to the cognition of the properties (potentially dangerous or safe) of the perceived signal.

This paper describes a method for discriminating of the human forearm motions based on the myoelectric signals using an adaptive fuzzy inference system. In conventional studies, the neural network is often used to estimate motion intention by the myoelectric signals and realizes the high discrimination precision. On the other hand, this study uses the fuzzy inference for a human forearm motion discrimination based on the myoelectric signals. This study designs the membership function and the fuzzy rules using the average value and the standard deviation of the root mean square of the myoelectric potential for every channel of each motion. In addition, the characteristics of the myoelectric potential gradually change as a result of the muscle fatigue. Therefore, the motion discrimination should be performed by taking muscle fatigue into consideration. This study proposes a method to redesign the fuzzy inference system such that dynamic change of the myoelectric potential because of the muscle fatigue will be taken into account. Some experiments carried out using a myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.

We studied human perceptual learning in the peripheral visual field in 16 healthy adults. Horizontal or vertical vernier stimuli were presented simultaneously at 8 locations at an eccentricity of 4 degrees . One of the stimuli displayed an offset, and subjects were asked to detect the target offset. Training was performed with either vertical or horizontal stimuli by the repeated presentation of stimuli. Discrimination performance was also measured with the untrained stimuli. Before and after the psychophysical experiment, EEG was recorded from 30 electrodes over the occipital areas (between the inion and Cz) while targets were presented at all locations as vernier onset/offset stimuli. The EEG was averaged for each orientation separately. Improvement in discrimination performance was observed in about 70% of the subjects with the trained orientation only. The evoked potential maps displayed three components occurring between 80 and 160, 180 and 260, and 280 and 340 ms. The potential field topography of the first and third component showed significant differences before and after learning. In addition, field strength (global field power) of the second and third component increased with learning. No effects were seen with the untrained stimuli in the psychophysical and electrophysiological experiments. Our findings suggest that perceptual learning in the peripheral visual field is specifically related to neurophysiological changes induced by training, and it is not caused by unspecific changes of spatial attention. The changes of electrical brain activity reflect short-term plasticity related to human perceptual learning.

Because amplitude- and frequency-modulated sounds can be the basis for the synthesis of many complex sounds, they can be good candidates in the design of training systems aiming at improving the acquisition of perceptual skills that can benefit from information provided via the auditory channel......-training, training, a post-training stages. During training, listeners were divided into two groups; one group trained on amplitude-modulation rate discrimination and the other group trained on frequency-modulation rate discrimination. Results will be discussed in terms of their implications for training...

Full Text Available using machine learning. Newer, advanced deep learning techniques offer a unique solution but traditionally require a large dataset to train effectively. Highway Networks allow for very deep networks that can be trained using the smaller datasets typical...

Full Text Available It is the learners' right to get an education free from discrimination. Discrimination in education ranges from gender to race, age, social class, financial status, and other characteristics. In this study the focus is on discrimination in education in regard to social class and financial status. The paper describes observations of the school building layout and corresponding activities and behaviours in language education classes. The researcher observed 10 English language classes from different districts during 10 years from 2004 through 2014 and took notes on the activities and behaviours provided in the classroom to identify whether there was any correspondence with educational behaviours. The investigation in this study concluded that the language classes of most of the public (state schools and some semi-private schools included a curriculum based on translation and memorization teaching methods. In these schools, learners exhibited stress and inattention that disturbed their learning. In these classes learners were threatened by laughing or rough criticizing by the teachers. The observation results were analyzed to make comparisons between schools and inform the level of equality in different schools.

Full Text Available The results of the application of an unsupervised learning (neural network approach comprising a Self Organizing Map (SOM, to distinguish micro-earthquakes from quarry blasts in the vicinity of Istanbul, Turkey, are presented and discussed. The SOM is constructed as a neural classifier and complementary reliability estimator to distinguish seismic events, and was employed for varying map sizes. Input parameters consisting of frequency and time domain data (complexity, spectral ratio, S/P wave amplitude peak ratio and origin time of events extracted from the vertical components of digital seismograms were estimated as discriminants for 179 (1.8 < Md < 3.0 local events. The results show that complexity and amplitude peak ratio parameters of the observed velocity seismogram may suffice for a reliable discrimination, while origin time and spectral ratio were found to be fuzzy and misleading classifiers for this problem. The SOM discussed here achieved a discrimination reliability that could be employed routinely in observatory practice; however, about 6% of all events were classified as ambiguous cases. This approach was developed independently for this particular classification, but it could be applied to different earthquake regions.

Although intelligence has traditionally been identified as "the ability to learn" (Peterson, 1925), this relationship has been questioned in simple operant learning tasks (Spielberger, 1962). Nevertheless, recent pieces of research have demonstrated a strong and significant correlation between associative learning measures and intelligence…

为了充分挖掘样本内在的几何结构和蕴含的判别信息来指导样本数据分类,提出一种局部敏感的判别直推学习方法.该方法将局部敏感辨析(LSDA)的基本原理引入到直推学习中,在直推学习的正则化框架中同时引入有助于分类的样本局部结构信息和判别信息,在判别信息指导下构建了类内图和类间图来刻画类内紧性和类间散性,从而在每个局部邻域中进一步最大化类间样本的间隔.同时,用数学的形式给出了目标函数的解析表达,在几个典型数据集上的实验结果表明,相较传统的基于图的半监督学习算法,该方法能取得更高的分类效果.%A novel approach called locality sensitive discriminant transductive learning (LSD-TL) was developed in order to utilize the underlying geometric structure and the discriminant information to full extent in the process of data classification. Inspired by the basic theories of the locality sensitive discriminant analysis (LSDA), LSD-TL directly incorporates the discriminative information as well as the local geometry of the data space into the regularization framework of transductive learning. LSD-TL constructs the within-class graph and the between-class graph under the guidance of the discriminative information, so that the margins between the data of different classes in each local manifold are maximized. In addition, the analytical solution for the problem was obtained. Experimental results on real world datasets demonstrated that compared with several traditional graph-based semi-supervised algorithms, the new approach improved the classification accuracy.

This study investigated whether individual behavioural characteristics of piglets and stress induced by experience with humans can influence learning performance. After weaning, piglets received a chronic experience with humans to modulate their emotional state: rough (ROU), gentle (GEN), or minimal (MIN) experience. Simultaneously, they were trained on a discrimination task. Afterward, their behaviour during challenge tests was assessed. The first learning step of the task involved associating a positive sound cue with a response (approach a trough) and success of piglets depended mostly on motivation to seek for reward. Although the experience with humans did not have direct effect, the degree of fear of handler, measured based on their reactivity to a human approach test, was related to motivation to seek rewards and learning speed of this first step in stressed ROU piglets, but not in MIN and GEN piglets. In contrast, the second learning step was more cognitively challenging, since it involved discriminationlearning, including negative cues during which piglets had to learn to avoid the trough. Locomotion activity, measured during an open-field test, was associated with performance of the discriminationlearning. To conclude, fearfulness towards humans and locomotion activity are linked with learning performance in relation to task complexity, highlighting the necessity to take into account these factors in animal research and management.

Stigma is a major barrier to the life opportunities of people with disabilities, including those with psychiatric disabilities. Structural discrimination is stigma that results from social forces that develop over many years to diminish a group's resources and support needed to be successful. Affirmative action is a legal and political remedy to…

Stigma is a major barrier to the life opportunities of people with disabilities, including those with psychiatric disabilities. Structural discrimination is stigma that results from social forces that develop over many years to diminish a group's resources and support needed to be successful. Affirmative action is a legal and political remedy to…

In video surveillance, the moving human detection in thermal video is a critical phase that filters out redundant information to extract relevant information. The moving object detection is applied on thermal video because it penetrate challenging problems such as dynamic issues of background and illumination variation. In this work, we have proposed a new background subtraction method using Fisher's linear discriminant ratio based threshold. This threshold is investigated automatically during run-time for each pixel of every sequential frame. Automatically means to avoid the involvement of external source such as programmer or user for threshold selection. This threshold provides better pixel classification at run-time. This method handles problems generated due to multiple behavior of background more accurately using Fisher's ratio. It maximizes the separation between object pixel and the background pixel. To check the efficacy, the performance of this work is observed in terms of various parameters depicted in analysis. The experimental results and their analysis demonstrated better performance of proposed method against considered peer methods.

Our long-term goal was to investigate the potential of incorporating redox imaging technique as a breast cancer (BC) diagnosis component to increase the positive predictive value of suspicious imaging finding and to reduce unnecessary biopsies and overdiagnosis. We previously found that precancer and cancer tissues in animal models displayed abnormal mitochondrial redox state. We also revealed abnormal mitochondrial redox state in cancerous specimens from three BC patients. Here, we extend our study to include biopsies of 16 patients. Tissue aliquots were collected from both apparently normal and cancerous tissues from the affected cancer-bearing breasts shortly after surgical resection. All specimens were snap-frozen and scanned with the Chance redox scanner, i.e., the three-dimensional cryogenic NADH/Fp (reduced nicotinamide adenine dinucleotide/oxidized flavoproteins) fluorescence imager. We found both Fp and NADH in the cancerous tissues roughly tripled that in the normal tissues (pcancerous tissues (pcancer with reasonable sensitivity and specificity. Our findings suggest that the optical redox imaging technique can provide parameters independent of clinical factors for discriminating cancer from noncancer breast tissues in human patients.

The prostate gland is the most common site of pathology in human males. Using the urethra as an anatomical reference point, it can be divided into three distinct zones known as the transition zone (TZ), peripheral zone (PZ) and central zone (CZ). The pathological conditions of benign prostatic hypertrophy and/or prostate adenocarcinoma are highly prevalent in this gland. This preliminary study set out to determine whether biochemical intra-individual differences between normal prostate zones could be identified using Raman spectroscopy with subsequent exploratory analyses. A normal (benign) prostate transverse tissue section perpendicular to the rectal surface and above the verumontanum was obtained in a paraffin-embedded block. A 10-µm-thick slice was floated onto a gold substrate, de-waxed and analysed using Raman spectroscopy (200 epithelial-cell and 140 stromal spectra/zone). Raman spectra were subsequently processed in the 1800-367 cm(-1) spectral region employing principal component analysis (PCA) to determine whether wavenumber-intensity relationships expressed as single points in hyperspace might reveal biochemical differences associated with inter-zone pathological susceptibility. Visualisation of PCA scores plots and their corresponding loadings plots highlighted 781 cm(-1) (cytosine/uracil) and 787 cm(-1) (DNA) as the key discriminating factors segregating PZ from less susceptible TZ and CZ epithelia (P prostate zones to specific pathological conditions.

Full Text Available Discrimination against women based on the fact that they are women is a deeply rooted practice in all societies. However, the level of discrimination varies greatly with the level of development of the given society and strongly influences and vice versa it is influenced by the status of women in a given society. Addressing this gender-based discrimination is a difficult task because it is closely linked to the concept of equality, and state’s action and inactions. The article establishes that the States parties’ obligation is to ensure that there is no direct or indirect discrimination against women in their laws, sanctions, and other remedies and those women are protected against discrimination in the public, as well as, in the private spheres.

Full Text Available Automatic target recognition (ATR in synthetic aperture radar (SAR images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification are key points in SAR ATR. In this paper, we first design a novel feature, which is a histogram of oriented gradients (HOG-like feature for SAR ATR (called SAR-HOG. Then, we propose a supervised discriminative dictionary learning (SDDL method to learn a discriminative dictionary for SAR ATR and propose a strategy to simplify the optimization problem. Finally, we propose a SAR ATR classifier based on SDDL and sparse representation (called SDDLSR, in which both the reconstruction error and the classification error are considered. Extensive experiments are performed on the MSTAR database under standard operating conditions and extended operating conditions. The experimental results show that SAR-HOG can reliably capture the structures of targets in SAR images, and SDDL can further capture subtle differences among the different classes. By virtue of the SAR-HOG feature and SDDLSR, the proposed method achieves the state-of-the-art performance on MSTAR database. Especially for the extended operating conditions (EOC scenario “Training 17 ∘ —Testing 45 ∘ ”, the proposed method improves remarkably with respect to the previous works.

Rey's Auditory Verbal Learning Test (RAVLT) is a widely used neuropsychological test to assess episodic memory. In the present study we sought to establish normative and discriminative validity data for the RAVLT in the elderly population using previously adapted learning lists for the Greek adult population. We administered the test to 258 cognitively healthy elderly participants, aged 60-89 years, and two patient groups (192 with amnestic mild cognitive impairment, aMCI, and 65 with Alzheimer's disease, AD). From the statistical analyses, we found that age and education contributed significantly to most trials of the RAVLT, whereas the influence of gender was not significant. Younger elderly participants with higher education outperformed the older elderly with lower education levels. Moreover, both clinical groups performed significantly worse on most RAVLT trials and composite measures than matched cognitively healthy controls. Furthermore, the AD group performed more poorly than the aMCI group on most RAVLT variables. Receiver operating characteristic (ROC) analysis was used to examine the utility of the RAVLT trials to discriminate cognitively healthy controls from aMCI and AD patients. Area under the curve (AUC), an index of effect size, showed that most of the RAVLT measures (individual and composite) included in this study adequately differentiated between the performance of healthy elders and aMCI/AD patients. We also provide cutoff scores in discriminating cognitively healthy controls from aMCI and AD patients, based on the sensitivity and specificity of the prescribed scores. Moreover, we present age- and education-specific normative data for individual and composite scores for the Greek adapted RAVLT in elderly subjects aged between 60 and 89 years for use in clinical and research settings.

In this paper we explore English pronunciation teaching within an English as an International Language (EIL) framework, arguing that teaching learners how to produce English phonemes can lead to an improvement in their aural ability. English as an Additional Language (EAL) learners often have difficulty discriminating between and producing…

This article makes a case for the use of blended learning in teaching human development as a means to encourage higher-order student learning outcomes. The authors review literature regarding the use and effectiveness of blended learning, discuss an illustrative example of a redesign of a human development course, present outcomes from a…

This article makes a case for the use of blended learning in teaching human development as a means to encourage higher-order student learning outcomes. The authors review literature regarding the use and effectiveness of blended learning, discuss an illustrative example of a redesign of a human development course, present outcomes from a…

Humans have the unique ability to create art, but non-human animals may be able to discriminate "good" art from "bad" art. In this study, I investigated whether pigeons could be trained to discriminate between paintings that had been judged by humans as either "bad" or "good". To do this, adult human observers first classified several children's paintings as either "good" (beautiful) or "bad" (ugly). Using operant conditioning procedures, pigeons were then reinforced for pecking at "good" paintings. After the pigeons learned the discrimination task, they were presented with novel pictures of both "good" and "bad" children's paintings to test whether they had successfully learned to discriminate between these two stimulus categories. The results showed that pigeons could discriminate novel "good" and "bad" paintings. Then, to determine which cues the subjects used for the discrimination, I conducted tests of the stimuli when the paintings were of reduced size or grayscale. In addition, I tested their ability to discriminate when the painting stimuli were mosaic and partial occluded. The pigeons maintained discrimination performance when the paintings were reduced in size. However, discrimination performance decreased when stimuli were presented as grayscale images or when a mosaic effect was applied to the original stimuli in order to disrupt spatial frequency. Thus, the pigeons used both color and pattern cues for their discrimination. The partial occlusion did not disrupt the discriminative behavior suggesting that the pigeons did not attend to particular parts, namely upper, lower, left or right half, of the paintings. These results suggest that the pigeons are capable of learning the concept of a stimulus class that humans name "good" pictures. The second experiment showed that pigeons learned to discriminate watercolor paintings from pastel paintings. The subjects showed generalization to novel paintings. Then, as the first experiment, size reduction test

A month-long workshop on medical humanities was held in the Jorhat Medical College, Assam in September 2015. It employed experiential learning (both online and onsite) using humanities tools, such as the theatre of the oppressed, art, literature, reflective narratives, movies, the history of medicine, graphic medicine, poetry and diversity studies. As a result of the interactions, 28 volunteer participants, comprising students and faculty members, wrote reflective narratives on doctor​-patient relationships, produced a newsletter and a logo for their medical humanities group, and staged cultural performances and forum theatre. The narratives, participants' reflections and feedback received were subjected to qualitative analysis; the workshop was evaluated using Kirkpatrick's model. The participants learned to examine their attitudes and behaviour, communicate with their bodies, and experience respect for diversity. There was an improvement in their understanding of empathy, ethics and professionalism. The workshop achieved level-3 (behaviour) on Kirkpatrick's model, suggesting that such workshops can initiate a change in the ABCDE attributes (attitude, behaviour, communication, diversity, ethics and empathy) of medical professionals.

Learning associations between co-occurring events enables us to extract structure from our environment. Medial-temporal lobe structures are critical for associative learning. However, the role of the ventral visual pathway (VVP) in associative learning is not clear. Do multivoxel object representations in the VVP reflect newly formed associations? We show that VVP multivoxel representations become more similar to each other after human participants learn arbitrary new associations between pairs of unrelated objects (faces, houses, cars, chairs). Participants were scanned before and after 15 days of associative learning. To evaluate how object representations changed, a classifier was trained on discriminating two nonassociated categories (e.g., faces/houses) and tested on discriminating their paired associates (e.g., cars/chairs). Because the associations were arbitrary and counterbalanced across participants, there was initially no particular reason for this cross-classification decision to tend toward either alternative. Nonetheless, after learning, cross-classification performance increased in the VVP (but not hippocampus), on average by 3.3%, with some voxels showing increases of up to 10%. For example, a chair multivoxel representation that initially resembled neither face nor house representations was, after learning, classified as more similar to that of faces for participants who associated chairs with faces and to that of houses for participants who associated chairs with houses. Additionally, learning produced long-lasting perceptual consequences. In a behavioral priming experiment performed several months later, the change in cross-classification performance was correlated with the degree of priming. Thus, VVP multivoxel representations are not static but become more similar to each other after associative learning.

The stress response is essential to the survival of all species as it maintains internal equilibrium and allows organisms to respond to threats in the environment. Most stress research has focused on the detrimental impacts of stress on cognition and behavior. Reversal learning, which requires a change in response strategy based on one dimension of the stimuli, is one type of behavioral flexibility that is facilitated following some brief stress procedures. The current study investigated a potential mechanism underlying this facilitation by blocking glucocorticoid receptors (GRs) during stress. Thirty-seven male Long Evans rats learned to discriminate between two images on a touchscreen, one of which was rewarded. Once a criterion was reached, rats received stress (30 min of restraint stress or no stress) and drug (GR antagonist RU38486 or vehicle) administration prior to each of the first 3 days of reversal learning. We expected that stress would facilitate reversal learning and RU38486 (10 mg/kg) would prevent this facilitation in both early (50% correct in one session) stages of reversal learning. Results showed that stressed rats performed better than unstressed rats (fewer days for late reversal, fewer correction trials, and fewer errors) in the late but not early stage of reversal learning. RU38486 did not block the facilitation of RL by stress, although it dramatically increased response, but not reward, latencies. These results confirm the facilitation of late reversal by stress in a touchscreen-based operant task in rats and further our understanding of how stress affects higher level cognitive functioning and behavior.

This paper presents a method for designing semi-supervised classifiers trained on labeled and unlabeled samples. We focus on probabilistic semi-supervised classifier design for multi-class and single-labeled classification problems, and propose a hybrid approach that takes advantage of generative and discriminative approaches. In our approach, we first consider a generative model trained by using labeled samples and introduce a bias correction model, where these models belong to the same model family, but have different parameters. Then, we construct a hybrid classifier by combining these models based on the maximum entropy principle. To enable us to apply our hybrid approach to text classification problems, we employed naive Bayes models as the generative and bias correction models. Our experimental results for four text data sets confirmed that the generalization ability of our hybrid classifier was much improved by using a large number of unlabeled samples for training when there were too few labeled samples to obtain good performance. We also confirmed that our hybrid approach significantly outperformed generative and discriminative approaches when the performance of the generative and discriminative approaches was comparable. Moreover, we examined the performance of our hybrid classifier when the labeled and unlabeled data distributions were different.

The desire to consume high volumes of fat is thought to originate from an evolutionary pressure to hoard calories, and fat is among the few energy sources that we can store over a longer time period. From an ecological perspective, however, it would be beneficial to detect fat from a distance, before ingesting it. Previous results indicate that humans detect high concentrations of fatty acids by their odor. More important though, would be the ability to detect fat content in real food products. In a series of three sequential experiments, using study populations from different cultures, we demonstrated that individuals are able to reliably detect fat content of food via odors alone. Over all three experiments, results clearly demonstrated that humans were able to detect minute differences between milk samples with varying grades of fat, even when embedded within a milk odor. Moreover, we found no relation between this performance and either BMI or dairy consumption, thereby suggesting that this is not a learned ability or dependent on nutritional traits. We argue that our findings that humans can detect the fat content of food via odors may open up new and innovative future paths towards a general reduction in our fat intake, and future studies should focus on determining the components in milk responsible for this effect.

Studies of sequential decision-making in humans frequently find suboptimal performance relative to an ideal actor that has perfect knowledge of the model of how rewards and events are generated in the environment. Rather than being suboptimal, we argue that the learning problem humans face is more complex, in that it also involves learning the structure of reward generation in the environment. We formulate the problem of structure learning in sequential decision tasks using Bayesian reinforcement learning, and show that learning the generative model for rewards qualitatively changes the behavior of an optimal learning agent. To test whether people exhibit structure learning, we performed experiments involving a mixture of one-armed and two-armed bandit reward models, where structure learning produces many of the qualitative behaviors deemed suboptimal in previous studies. Our results demonstrate humans can perform structure learning in a near-optimal manner.

Full Text Available Studies of sequential decision-making in humans frequently find suboptimal performance relative to an ideal actor that has perfect knowledge of the model of how rewards and events are generated in the environment. Rather than being suboptimal, we argue that the learning problem humans face is more complex, in that it also involves learning the structure of reward generation in the environment. We formulate the problem of structure learning in sequential decision tasks using Bayesian reinforcement learning, and show that learning the generative model for rewards qualitatively changes the behavior of an optimal learning agent. To test whether people exhibit structure learning, we performed experiments involving a mixture of one-armed and two-armed bandit reward models, where structure learning produces many of the qualitative behaviors deemed suboptimal in previous studies. Our results demonstrate humans can perform structure learning in a near-optimal manner.

Recognition of temporal/dynamical patterns is among the most difficult pattern recognition tasks. Human gait recognition is a typical difficulty in the area of dynamical pattern recognition. It classifies and identifies individuals by their time-varying gait signature data. Recently, a new dynamical pattern recognition method based on deterministic learning theory was presented, in which a time-varying dynamical pattern can be effectively represented in a time-invariant manner and can be rapidly recognized. In this paper, we present a new model-based approach for human gait recognition via the aforementioned method, specifically for recognizing people by gait. The approach consists of two phases: a training (learning) phase and a test (recognition) phase. In the training phase, side silhouette lower limb joint angles and angular velocities are selected as gait features. A five-link biped model for human gait locomotion is employed to demonstrate that functions containing joint angle and angular velocity state vectors characterize the gait system dynamics. Due to the quasi-periodic and symmetrical characteristics of human gait, the gait system dynamics can be simplified to be described by functions of joint angles and angular velocities of one side of the human body, thus the feature dimension is effectively reduced. Locally-accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks (NNs) through deterministic learning. The obtained knowledge of the approximated gait system dynamics is stored in constant RBF networks. A gait signature is then derived from the extracted gait system dynamics along the phase portrait of joint angles versus angular velocities. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. In the test phase, by comparing the set of estimators with the test gait pattern, a set of recognition errors are generated, and the average L(1) norms

Full Text Available The corneal endothelium is a monolayer of hexagonal corneal endothelial cells (CECs on the inner surface of the cornea. CECs are critical in maintaining corneal transparency through their barrier and pump functions. CECs in vivo have a limited capacity in proliferation, and loss of a significant number of CECs results in corneal edema called bullous keratopathy which can lead to severe visual loss. Corneal transplantation is the most effective method to treat corneal endothelial dysfunction, where it suffers from donor shortage. Therefore, regeneration of CECs from other cell types attracts increasing interests, and specific markers of CECs are crucial to identify actual CECs. However, the currently used markers are far from satisfactory because of their non-specific expression in other cell types. Here, we explored molecular markers to discriminate CECs from other cell types in the human body by integrating the published RNA-seq data of CECs and the FANTOM5 atlas representing diverse range of cell types based on expression patterns. We identified five genes, CLRN1, MRGPRX3, HTR1D, GRIP1 and ZP4 as novel markers of CECs, and the specificities of these genes were successfully confirmed by independent experiments at both the RNA and protein levels. Notably none of them have been documented in the context of CEC function. These markers could be useful for the purification of actual CECs, and also available for the evaluation of the products derived from other cell types. Our results demonstrate an effective approach to identify molecular markers for CECs and open the door for the regeneration of CECs in vitro.

The corneal endothelium is a monolayer of hexagonal corneal endothelial cells (CECs) on the inner surface of the cornea. CECs are critical in maintaining corneal transparency through their barrier and pump functions. CECs in vivo have a limited capacity in proliferation, and loss of a significant number of CECs results in corneal edema called bullous keratopathy which can lead to severe visual loss. Corneal transplantation is the most effective method to treat corneal endothelial dysfunction, where it suffers from donor shortage. Therefore, regeneration of CECs from other cell types attracts increasing interests, and specific markers of CECs are crucial to identify actual CECs. However, the currently used markers are far from satisfactory because of their non-specific expression in other cell types. Here, we explored molecular markers to discriminate CECs from other cell types in the human body by integrating the published RNA-seq data of CECs and the FANTOM5 atlas representing diverse range of cell types based on expression patterns. We identified five genes, CLRN1, MRGPRX3, HTR1D, GRIP1 and ZP4 as novel markers of CECs, and the specificities of these genes were successfully confirmed by independent experiments at both the RNA and protein levels. Notably none of them have been documented in the context of CEC function. These markers could be useful for the purification of actual CECs, and also available for the evaluation of the products derived from other cell types. Our results demonstrate an effective approach to identify molecular markers for CECs and open the door for the regeneration of CECs in vitro.

The majority of theoretical models of learning consider learning to be a continuous function of experience. However, most perceptual learning studies use thresholds estimated by fitting psychometric functions to independent blocks, sometimes then fitting a parametric function to these block-wise estimated thresholds. Critically, such approaches tend to violate the basic principle that learning is continuous through time (e.g., by aggregating trials into large "blocks" for analysis that each assume stationarity, then fitting learning functions to these aggregated blocks). To address this discrepancy between base theory and analysis practice, here we instead propose fitting a parametric function to thresholds from each individual trial. In particular, we implemented a dynamic psychometric function whose parameters were allowed to change continuously with each trial, thus parameterizing nonstationarity. We fit the resulting continuous time parametric model to data from two different perceptual learning tasks. In nearly every case, the quality of the fits derived from the continuous time parametric model outperformed the fits derived from a nonparametric approach wherein separate psychometric functions were fit to blocks of trials. Because such a continuous trial-dependent model of perceptual learning also offers a number of additional advantages (e.g., the ability to extrapolate beyond the observed data; the ability to estimate performance on individual critical trials), we suggest that this technique would be a useful addition to each psychophysicist's analysis toolkit.

Amplifying human ability for controlling complex environments featuring autonomous units can be aided by learned models of human and system performance. In developing a command and control system that allows a small number of people to control a large number of autonomous teams, we employ an autonomics framework to manage the networks that represent mission plans and the networks that are composed of human controllers and their autonomous assistants. Machine learning allows us to build models of human and system performance useful for monitoring plans and managing human attention and task loads. Machine learning also aids in the development of tactics that human supervisors can successfully monitor through the command and control system.

estimation. Moreover, a criterion that summarizes all the produced values of AD is employed with a GA (Genetic Algorithm)-based approach to obtain the optimum feature ordering for classification problems based on neural networks by means of IAL. Compared with the feature ordering obtained by other approaches......, the method proposed in this paper exhibits better performance in the final classification results. Such a phenomenon indicates that, (i) the feature discrimination ability should be incrementally estimated in IAL, and (ii) the feature ordering derived by AD and its corresponding approaches are applicable...

Pigeons were trained on two temporal bisection tasks, which alternated every two sessions. In the first task, they learned to choose a red key after a 1-s signal and a green key after a 4-s signal; in the second task, they learned to choose a blue key after a 4-s signal and a yellow key after a 16-s signal. Then the pigeons were exposed to a series of test trials in order to contrast two timing models, Learning-to-Time (LeT) and Scalar Expectancy Theory (SET). The models made substantially different predictions particularly for the test trials in which the sample duration ranged from 1 s to 16 s and the choice keys were Green and Blue, the keys associated with the same 4-s samples: LeT predicted that preference for Green should increase with sample duration, a context effect, but SET predicted that preference for Green should not vary with sample duration. The results were consistent with LeT. The present study adds to the literature the finding that the context effect occurs even when the two basic discriminations are never combined in the same session.

in a modification of (4), that allows sparse representations under bounded noise, known as basis pursuit denoising [21], or the Lasso [22]. Its...the atoms of the dictionary. There are several ways for learning these atoms. The K- SVD and Method of Orthogonal Directions (MOD) algorithms [25...Computation, vol. 12, no. 2, pp. 337–365, 2000. [27] M. Elad and M. Aharon, “Image denoising via learned dictionaries and sparse representation,” in

Research has suggested that reduced working memory capacity plays a key role in disinhibited patterns of behavior associated with externalizing psychopathology. In this study, participants (N = 365) completed 2 versions of a go/no-go mixed-incentive learning task that differed in the relative frequency of monetary rewards and punishments for correct and incorrect active-approach responses, respectively. Using separate structural equation models for conventional (hit and false alarm rates) and signal detection theory (signal discriminability and response bias) performance indices, distinct roles for working memory capacity and changes in payoff structure were found. Specifically, results showed that (a) working memory capacity mediated the effects of externalizing psychopathology on false alarms and discriminability of go versus no-go signals; (b) these effects were not moderated by the relative frequency of monetary rewards and punishments; (c) the relative frequency of monetary rewards and punishments moderated the effects of externalizing psychopathology on hits and response bias for go versus no-go responses; and (d) these effects were not mediated by working memory capacity. The findings implicate distinct roles for reduced working memory capacity and poorly modulated active approach and passive avoidance in the link between externalizing psychopathology and behavioral disinhibition.

Memory formation following the one trial discriminated bead task in the chick falls into three stages (short-term, intermediate and long-term memory) that are defined by susceptibility to different classes of drugs. The stages show sharply timed offsets of sensitivity and loss at specific times after inhibition. Recall of the memory in the chick shows cyclical changes that differ in period between left and right hemispheres, and is marked by a series of brief windows of enhanced recall that recur with periods of about 16 and 25 min in the left and right hemispheres respectively. The timing of these 'retrieval events' corresponds, to a large extent, with the timing of the memory stages seen in the visual discrimination task. Here we examine the effects of left or right hemisphere injection of the main agents (glutamate, ouabain and anisomycin) that have been used to characterize the three stages of memory. We show that memory in the left hemisphere is largely responsible for performance at test and that processes involved in its consolidation generate the phases of memory.

Full Text Available Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e. discriminative information may be less relevant than information regarding the relative location of two objects (i.e. relative information. For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action.We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in early visual cortex, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS, but not from EVC, where we find the spatial representation to be warped.We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model’s receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC.These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representation into relative spatial representation along the visual stream.

Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream.

Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream. PMID:27242455

This dissertation focuses on the application of learning technology standards for learning objects and the differences in reuse in university, corporate, and military contexts. This is addressed from two different perspectives: the technology involving learning objects and the human aspects that

Training-induced neuronal activity develops in the mammalian limbic system during discriminative avoidance conditioning. This study explores behaviorally relevant changes in muscarinic ACh receptor binding in 52 rabbits that were trained to one of five stages of conditioned response acquisition. Sixteen naive and 10 animals yoked to criterion performance served as control cases. Upon reaching a particular stage of training, the brains were removed and autoradiographically assayed for 3H-oxotremorine-M binding with 50 nM pirenzepine (OxO-M/PZ) or for 3H-pirenzepine binding in nine limbic thalamic nuclei and cingulate cortex. Specific OxO-M/PZ binding increased in the parvocellular division of the anterodorsal nucleus early in training when the animals were first exposed to pairing of the conditional and unconditional stimuli. Elevated binding in this nucleus was maintained throughout subsequent training. In the parvocellular division of the anteroventral nucleus (AVp), OxO-M/PZ binding progressively increased throughout training, reached a peak at the criterion stage of performance, and returned to control values during extinction sessions. Peak OxO-M/PZ binding in AVp was significantly elevated over that for cases yoked to criterion performance. In the magnocellular division of the anteroventral nucleus (AVm), OxO-M/PZ binding was elevated only during criterion performance of the task, and it was unaltered in any other limbic thalamic nuclei. Specific OxO-M/PZ binding was also elevated in most layers in rostral area 29c when subjects first performed a significant behavioral discrimination. Training-induced alterations in OxO-M/PZ binding in AVp and layer Ia of area 29c were similar and highly correlated.

textabstractThe choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann ma

Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking, because they require very long training time and a large number of training samples. In this paper, we present an efficient and very robust tracking algorithm using a single convolutional neural network (CNN) for learning effective feature representations of the target object in a purely online manner. Our contributions are multifold. First, we introduce a novel truncated structural loss function that maintains as many training samples as possible and reduces the risk of tracking error accumulation. Second, we enhance the ordinary stochastic gradient descent approach in CNN training with a robust sample selection mechanism. The sampling mechanism randomly generates positive and negative samples from different temporal distributions, which are generated by taking the temporal relations and label noise into account. Finally, a lazy yet effective updating scheme is designed for CNN training. Equipped with this novel updating algorithm, the CNN model is robust to some long-existing difficulties in visual tracking, such as occlusion or incorrect detections, without loss of the effective adaption for significant appearance changes. In the experiment, our CNN tracker outperforms all compared state-of-the-art methods on two recently proposed benchmarks, which in total involve over 60 video sequences. The remarkable performance improvement over the existing trackers illustrates the superiority of the feature representations, which are learned purely online via the proposed deep learning framework.

We have used a genetically tractable model system, the fruit fly "Drosophila melanogaster" to study the interdependence between sensory processing and associative processing on learning performance. We investigated the influence of variations in the physical and predictive properties of color stimuli in several different operant-conditioning…

Feature extraction and learning is critical for object recognition and detection. By embedding context cue of image attributes into the kernel descriptors, we propose a set of novel kernel descriptors called context kernel descriptors (CKD). The motivation of CKD is to use the spatial consistency...

textabstractThe choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann

The present studies examined the role of linguistic experience in directing English and Mandarin learners' attention to aspects of a visual scene. Specifically, they asked whether young language learners in these 2 cultures attend to differential aspects of a word-learning situation. Two groups of English and Mandarin learners, 6-8-month-olds (n =…

Infants' perception of faces becomes attuned to the environment during the first year of life. However, the mechanisms that underpin perceptual narrowing for faces are only poorly understood. Considering the developmental similarities seen in perceptual narrowing for faces and speech and the role that statistical learning has been shown to play…

Once trained, rats are able to execute particularly difficult olfactory discrimination tasks with exceptional accuracy. Such skill acquisition, termed "rule learning", is accompanied by a series of long-lasting modifications to three cellular properties which modulate pyramidal neuron activity in piriform cortex; intrinsic excitability, synaptic excitation, and synaptic inhibition. Here, we explore how these changes, which are seemingly contradictory at the single-cell level in terms of their effect on neuronal excitation, are manifested within the piriform cortical neuronal network to store the memory of the rule, while maintaining network stability. To this end, we monitored network activity via multisite extracellular recordings of field postsynaptic potentials (fPSPS) and with single-cell recordings of miniature inhibitory and excitatory synaptic events in piriform cortex slices. We show that although 5 days after rule learning the cortical network maintains its basic activity patterns, synaptic connectivity is strengthened specifically between spatially proximal cells. Moreover, while the enhancement of inhibitory and excitatory synaptic connectivity is nearly identical, strengthening of synaptic inhibition is equally distributed between neurons while synaptic excitation is particularly strengthened within a specific subgroup of cells. We suggest that memory for the acquired rule is stored mainly by strengthening excitatory synaptic connection between close pyramidal neurons and runaway synaptic activity arising from this change is prevented by a nonspecific enhancement of synaptic inhibition.

Full Text Available The extent to which human speech perception evolved by taking advantage of predispositions and pre-existing features of vertebrate auditory and cognitive systems remains a central question in the evolution of speech. This paper reviews asymmetries in vowel perception, speaker voice recognition, and speaker normalization in non-human animals – topics that have not been thoroughly discussed in relation to the abilities of non-human animals, but are nonetheless important aspects of vocal perception. Throughout this paper we demonstrate that addressing these issues in non-human animals is relevant and worthwhile because many non-human animals must deal with similar issues in their natural environment. That is, they must also discriminate between similar-sounding vocalizations, determine signaler identity from vocalizations, and resolve signaler-dependent variation in vocalizations from conspecifics. Overall, we find that, although plausible, the current evidence is insufficiently strong to conclude that directional asymmetries in vowel perception are specific to humans, or that non-human animals can use voice characteristics to recognize human individuals. However, we do find some indication that non-human animals can normalize speaker differences. Accordingly, we identify avenues for future research that would greatly improve and advance our understanding of these topics.

The ability to induce broadly neutralizing antibody (bNAb) responses is likely essential for development of a globally effective HIV vaccine. Unfortunately, human vaccine trials conducted to date have failed to elicit broad plasma neutralization of primary virus isolates. Despite this limitation, in-depth analysis of the vaccine-induced memory B-cell repertoire can provide valuable insights into the presence and function of subdominant B-cell responses, and identify initiation of antibody lineages that may be on a path towards development of neutralization breadth. Characterization of the functional capabilities of monoclonal antibodies isolated from a HIV-1 vaccine trial with modest efficacy has revealed mechanisms by which non-neutralizing antibodies are presumed to have mediated protection. In addition, B-cell repertoire analysis has demonstrated that vaccine boosts shifted the HIV-specific B-cell repertoire, expanding pools of cells with long third heavy chain complementarity determining regions - a characteristic of some bNAb lineages. Detailed analysis of memory B-cell repertoires and evaluating the effector functions of isolated monoclonal antibodies expands what we can learn from human vaccine trails, and may provide knowledge that can enable rational design of novel approaches to drive maturation of subdominant disfavored bNAb lineages.

Machine-learning methods are evaluated to study the intriguing and debated topic of discrimination among different tectonic environments using geochemical and isotopic data. Volcanic rocks characterized by a whole geochemical signature of major elements (SiO2, TiO2, Al2O3, Fe2O3T, CaO, MgO, Na2O, K2O), selected trace elements (Sr, Ba, Rb, Zr, Nb, La, Ce, Nd, Hf, Sm, Gd, Y, Yb, Lu, Ta, Th) and isotopes (206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb, 87Sr/86Sr and 143Nd/144Nd) have been extracted from open-access and comprehensive petrological databases (i.e., PetDB and GEOROC). The obtained dataset has been analyzed using support vector machines, a set of supervised machine-learning methods, which are considered particularly powerful in classification problems. Results from the application of the machine-learning methods show that the combined use of major, trace elements and isotopes allows associating the geochemical composition of rocks to the relative tectonic setting with high classification scores (93 %, on average). The lowest scores are recorded from volcanic rocks deriving from back-arc basins (65 %). All the other tectonic settings display higher classification scores, with oceanic islands reaching values up to 99 %. Results of this study could have a significant impact in other petrological studies potentially opening new perspectives for petrologists and geochemists. Other examples of applications include the development of more robust geothermometers and geobarometers and the recognition of volcanic sources for tephra layers in tephro-chronological studies.

In this paper, surface-enhanced Raman spectroscopy (SERS) was used to detect the changes in blood serum components that accompany rectal cancer. The differences in serum SERS data between rectal cancer patients and healthy controls were examined. Postoperative rectal cancer patients also participated in the comparison to monitor the effects of cancer treatments. The results show that there are significant variations at certain wavenumbers which indicates alteration of corresponding biological substances. Principal component analysis (PCA) and parameters of intensity ratios were used on the original SERS spectra for the extraction of featured variables. These featured variables then underwent linear discriminant analysis (LDA) and classification and regression tree (CART) for the discrimination analysis. Accuracies of 93.5 and 92.4 % were obtained for PCA-LDA and parameter-CART, respectively.

Full Text Available The aim of the paper is to map the area where the social construction of age discrimination in the recruiting process is perceived as taking place, especially those individuals or organized groups with enough power and interest to influence this unethical reality. The research was carried out in 2010 and 2011 in Cluj-Napoca, Romania; it uses multiple qualitative methods (focus-group and interviews and covers three layers of perception: candidate’s perception, employer’s perception and recruiter’s perception. Usually, the main social actors publically perceived as influencing age discrimination in the recruiting process are the employers (as the main responsible, some public institutions (as guardians and the candidates (as victims. The findings of the paper show that the number of social actors perceived as interested and with power by the main social actors (employers and candidates is much higher than the number classically targeted by researchers, reaching 20 or more

... in Genetics Archive Regulation of Genetic Tests Genetic Discrimination Overview Many Americans fear that participating in research ... I) and employment (Title II). Read more Genetic Discrimination and Other Laws Genetic Discrimination and Other Laws ...

Full Text Available Previous research showed that Transcranial Direct Current Stimulation (tDCS can modulate visual cortex excitability. However, there is no experiment on the effects of tDCS on color perception to date. The present study aimed to investigate the effects of tDCS on color discrimination tasks. 15 healthy subjects (mean age of 25.6 ± 4.4 years were tested with Cambridge Color Test 2.0 (Trivector and Ellipses protocols and a Forced-choice Spatial Color Contrast Sensitivity task (vertical red-green sinusoidal grating while receiving tDCS. Anodal, cathodal and sham tDCS were delivered at Oz for 22 minutes using two square electrodes (25cm2 with a current of 1.5mA in sessions separated by 7 days. Anodal tDCS significantly increased tritan sensitivity (p<0.01 and had no significant effect on protan, deutan or red-green grating discrimination. The effects on the tritan discrimination returned to baseline after 15 minutes (p<0.01. Cathodal tDCS reduced the sensitivity in the deutan axis and increased sensitivity in the tritan axis (p<0.05. The lack of anodal tDCS effects in the protan, deutan and red-green grating sensitivities could be explained by a ceiling effect since adults in this age range tend to have optimal color discrimination performance for these hues. The differential effects of cathodal tDCS on tritan and deutan sensitivities and the absence of the proposed ceiling effects for the tritan axes might be explained by Parvocellular (P and Koniocellular (K systems with regard to their functional, physiological and anatomical differences. The results also support the existence of a systematic segregation of P and K color-coding cells in V1. Future research and possible clinical implications are discussed.

It is the learners' right to get an education free from discrimination. Discrimination in education ranges from gender to race, age, social class, financial status, and other characteristics. In this study the focus is on discrimination in education in regard to social class and financial status. The paper describes observations of the school…

Full Text Available The ventrolateral prefrontal cortex (VLPFC and amygdala have critical roles in the generation and regulation of unpleasant emotions, and in this study the dynamic neural basis of unpleasant emotion processing was elucidated by using paired-samples permutation t tests to identify the timing of emotional discrimination in various brain regions. We recorded the temporal dynamics of blood-oxygen-level-dependent signals in those brain regions during the viewing of unpleasant pictures by using functional magnetic resonance imaging with high temporal resolution, and we compared the time course of the signal within the volume of interest across emotional conditions. Results show that emotional discrimination in the right amygdala precedes that in the left amygdala and that emotional discrimination in both those regions precedes that in the right anterior VLPFC. They support the hypotheses that the right amygdala is part of a rapid emotional stimulus detection system and the left amygdala is specialized for sustained stimulus evaluation and that the right anterior VLPFC is implicated in the integration of viscerosensory information with affective signals between the bilateral anterior VLPFCs and the bilateral amygdalae.

Learning through experience underlies the ability to adapt to novel tasks and unfamiliar environments. However, learning must be regulated so that relevant aspects of the environment are selectively encoded. Acetylcholine (ACh) has been suggested to regulate learning by enhancing the responses of sensory cortical neurons to behaviorally relevant stimuli. In this study, we increased synaptic levels of ACh in the brains of healthy human subjects with the cholinesterase inhibitor donepezil (trade name: Aricept) and measured the effects of this cholinergic enhancement on visual perceptual learning. Each subject completed two 5 day courses of training on a motion direction discrimination task, once while ingesting 5 mg of donepezil before every training session and once while placebo was administered. We found that cholinergic enhancement augmented perceptual learning for stimuli having the same direction of motion and visual field location used during training. In addition, perceptual learning with donepezil was more selective to the trained direction of motion and visual field location. These results, combined with previous studies demonstrating an increase in neuronal selectivity following cholinergic enhancement, suggest a possible mechanism by which ACh augments neural plasticity by directing activity to populations of neurons that encode behaviorally relevant stimulus features.

Full Text Available This special issue is dedicated to technology-enabled approaches for improving higher education, adult learning, and human performance. Improvement of learning and human development for sustainable development has been recognized as a key strategy for individuals, institutions, and organizations to strengthen their competitive advantages. It becomes crucial to help adult learners and knowledge workers to improve their self-directed and life-long learning capabilities. Meanwhile, advances in technology have been increasingly enabling and facilitating learning and knowledge-related initiatives.. They have largely extended learning opportunities through the provision of resource-rich and learner-centered environment, computer-based learning support, and expanded social interactions and networks. Papers in this special issue are representative of ongoing research on integration of technology with learning for innovation and sustainable development in higher education institutions and organizational and community environments.

Recent proposals have conceptualized piriform cortex as an association cortex, capable of integrating incoming olfactory information with descending input from higher order associative regions such as orbitofrontal cortex and basolateral amygdala (ABL). If true, encoding in piriform cortex should reflect associative features prominent in these areas during associative learning involving olfactory cues. We recently reported that neurons in anterior piriform cortex (APC) in rats exhibited significant plasticity in their responses to odor cues during associative learning. Here, we have repeated this study, recording from neurons in posterior piriform cortex (PPC), a region of piriform cortex that receives much stronger input from ABL. If associative encoding in piriform cortex is driven by inputs from ABL, then we should see more plasticity in PPC neurons than we observed in APC. Consistent with this hypothesis, we found that PPC neurons were highly associative and appeared to be somewhat more likely than neurons recorded in APC to alter their responses to the odor cues after reversal of the odor-outcome associations in the task. Further, odor-selective PPC populations exhibited markedly different firing patterns based on the valence of the odor cue. These results suggest associative encoding in piriform cortex is represented in a topographical fashion, reflecting the stronger and more specific input from olfactory bulb concerning the sensory features of odors in anterior regions and stronger input from ABL concerning the meaning of odors in posterior regions.

In this paper, we address the problem of unsupervised domain transfer learning in which no labels are available in the target domain. We use a transformation matrix to transfer both the source and target data to a common subspace, where each target sample can be represented by a combination of source samples such that the samples from different domains can be well interlaced. In this way, the discrepancy of the source and target domains is reduced. By imposing joint low-rank and sparse constraints on the reconstruction coefficient matrix, the global and local structures of data can be preserved. To enlarge the margins between different classes as much as possible and provide more freedom to diminish the discrepancy, a flexible linear classifier (projection) is obtained by learning a non-negative label relaxation matrix that allows the strict binary label matrix to relax into a slack variable matrix. Our method can avoid a potentially negative transfer by using a sparse matrix to model the noise and, thus, is more robust to different types of noise. We formulate our problem as a constrained low-rankness and sparsity minimization problem and solve it by the inexact augmented Lagrange multiplier method. Extensive experiments on various visual domain adaptation tasks show the superiority of the proposed method over the state-of-the art methods. The MATLAB code of our method will be publicly available at http://www.yongxu.org/lunwen.html.

Full Text Available Organisational learning has become increasingly important for strategic renewal. Ambidextrous organisations are especially successful in the current environment, where firms are required to be efficient and adapt to change. Using a structural approach, this study discusses arguments about the nature of ambidexterity and identifies the kinds of human capital that better support specific learning types and HRM practices suited to these components of human capital. Results highlight learning differences between marketing and production units, as well as different HRM practices and human capital orientations. This study points out that human capital mediates between HRM practices and learning.

The development of sophisticated sensor technologies gave rise to an interesting variety of data. With the appearance of affordable devices, such as the Microsoft Kinect, depth-maps and three-dimensional data became easily accessible. This attracted many computer vision researchers seeking to exploit this information in classification and recognition tasks. In this work, the problem of face classification in the context of RGB images and depth information (RGB-D images) is addressed. The purpose of this paper is to study and compare some popular techniques for gender recognition and ethnicity classification to understand how much depth data can improve the quality of recognition. Furthermore, we investigate which combination of face descriptors, feature selection methods, and learning techniques is best suited to better exploit RGB-D images. The experimental results show that depth data improve the recognition accuracy for gender and ethnicity classification applications in many use cases.

Full Text Available Detecting motor imagery activities versus non-control in brain signals is the basis of self-paced brain-computer interfaces (BCIs, but also poses a considerable challenge to signal processing due to the complex and non-stationary characteristics of motor imagery as well as non-control. This paper presents a self-paced BCI based on a robust learning mechanism that extracts and selects spatio-spectral features for differentiating multiple EEG classes. It also employs a nonlinear regression and post-processing technique for predicting the time-series of class labels from the spatio-spectral features. The method was validated in the {BCI Competition IV} on {Dataset I} where it produced the lowest prediction error of class labels continuously. This report also presents and discusses analysis of the method using the competition data set.

Many computational models assume that reinforcement learning relies on changes in synaptic efficacy between cortical regions representing stimuli and striatal regions involved in response selection, but this assumption has thus far lacked empirical support in humans. We recorded hemodynamic signals with fMRI while participants navigated a virtual maze to find hidden rewards. We fitted a reinforcement-learning algorithm to participants' choice behavior and evaluated the neural activity and the changes in functional connectivity related to trial-by-trial learning variables. Activity in the posterior putamen during choice periods increased progressively during learning. Furthermore, the functional connections between the sensorimotor cortex and the posterior putamen strengthened progressively as participants learned the task. These changes in corticostriatal connectivity differentiated participants who learned the task from those who did not. These findings provide a direct link between changes in corticostriatal connectivity and learning, thereby supporting a central assumption common to several computational models of reinforcement learning.

In their predominantly theoretical turn to the material, post-humanist feminists often focus on "nature", arguing that the nature/culture binary has collapsed and that fixed distinctions between human and non-human spheres no longer hold. Conversely, outdoor learning sees nature as a space where humans act and has been more concerned…

In their predominantly theoretical turn to the material, post-humanist feminists often focus on "nature", arguing that the nature/culture binary has collapsed and that fixed distinctions between human and non-human spheres no longer hold. Conversely, outdoor learning sees nature as a space where humans act and has been more concerned…

Analyzing protein-protein interactions at the atomic level is critical for our understanding of the principles governing the interactions involved in protein-protein recognition. For this purpose, descriptors explaining the nature of different protein-protein complexes are desirable. In this work, the authors introduced Epic Protein Interface Classification as a framework handling the preparation, processing, and analysis of protein-protein complexes for classification with machine learning algorithms. We applied four different machine learning algorithms: Support Vector Machines, C4.5 Decision Trees, K Nearest Neighbors, and Naïve Bayes algorithm in combination with three feature selection methods, Filter (Relief F), Wrapper, and Genetic Algorithms, to extract discriminating features from the protein-protein complexes. To compare protein-protein complexes to each other, the authors represented the physicochemical characteristics of their interfaces in four different ways, using two different atomic contact vectors, DrugScore pair potential vectors and SFCscore descriptor vectors. We classified two different datasets: (A) 172 protein-protein complexes comprising 96 monomers, forming contacts enforced by the crystallographic packing environment (crystal contacts), and 76 biologically functional homodimer complexes; (B) 345 protein-protein complexes containing 147 permanent complexes and 198 transient complexes. We were able to classify up to 94.8% of the packing enforced/functional and up to 93.6% of the permanent/transient complexes correctly. Furthermore, we were able to extract relevant features from the different protein-protein complexes and introduce an approach for scoring the importance of the extracted features. (c) 2006 Wiley-Liss, Inc.

Currently, 65% of Americans are overweight, which leads to well-supported cardiovascular and cognitive declines. Little, however, is known concerning obesity's impact on sensory systems. Because olfaction is linked with ingestive behavior to guide food choice, its potential dysfunction during obesity could evoke a positive feedback loop to perpetuate poor ingestive behaviors. To determine the effect of chronic energy imbalance and reveal any structural or functional changes associated with obesity, we induced long-term, diet-induced obesity by challenging mice to high-fat diets: (1) in an obesity-prone (C57BL/6J) and obesity-resistant (Kv1.3(-/-)) line of mice, and compared this with (2) late-onset, genetic-induced obesity in MC4R(-/-) mice in which diabetes secondarily precipitates after disruption of the hypothalamic axis. We report marked loss of olfactory sensory neurons and their axonal projections after exposure to a fatty diet, with a concomitant reduction in electro-olfactogram amplitude. Loss of olfactory neurons and associated circuitry is linked to changes in neuronal proliferation and normal apoptotic cycles. Using a computer-controlled, liquid-based olfactometer, mice maintained on fatty diets learn reward-reinforced behaviors more slowly, have deficits in reversal learning demonstrating behavioral inflexibility, and exhibit reduced olfactory discrimination. When obese mice are removed from their high-fat diet to regain normal body weight and fasting glucose, olfactory dysfunctions are retained. We conclude that chronic energy imbalance therefore presents long-lasting structural and functional changes in the operation of the sensory system designed to encode external and internal chemical information and leads to altered olfactory- and reward-driven behaviors.

Full Text Available Selenium and sulfur are two closely related basic elements utilized in nature for a vast array of biochemical reactions. While toxic at higher concentrations, selenium is an essential trace element incorporated into selenoproteins as selenocysteine (Sec, the selenium analogue of cysteine (Cys. Sec lyases (SCLs and Cys desulfurases (CDs catalyze the removal of selenium or sulfur from Sec or Cys and generally act on both substrates. In contrast, human SCL (hSCL is specific for Sec although the only difference between Sec and Cys is the identity of a single atom. The chemical basis of this selenium-over-sulfur discrimination is not understood. Here we describe the X-ray crystal structure of hSCL and identify Asp146 as the key residue that provides the Sec specificity. A D146K variant resulted in loss of Sec specificity and appearance of CD activity. A dynamic active site segment also provides the structural prerequisites for direct product delivery of selenide produced by Sec cleavage, thus avoiding release of reactive selenide species into the cell. We thus here define a molecular determinant for enzymatic specificity discrimination between a single selenium versus sulfur atom, elements with very similar chemical properties. Our findings thus provide molecular insights into a key level of control in human selenium and selenoprotein turnover and metabolism.

Full Text Available This paper deals with the recognition of different hand gestures through machine learning approaches and principal component analysis. A Bio-Medical signal amplifier is built after doing a software simulation with the help of NI Multisim. At first a couple of surface electrodes are used to obtain the Electro-Myo-Gram (EMG signals from the hands. These signals from the surface electrodes have to be amplified with the help of the Bio-Medical Signal amplifier. The Bio-Medical Signal amplifier used is basically an Instrumentation amplifier made with the help of IC AD 620.The output from the Instrumentation amplifier is then filtered with the help of a suitable Band-Pass Filter. The output from the Band Pass filter is then fed to an Analog to Digital Converter (ADC which in this case is the NI USB 6008.The data from the ADC is then fed into a suitable algorithm which helps in recognition of the different hand gestures. The algorithm analysis is done in MATLAB. The results shown in this paper show a close to One-hundred per cent (100% classification result for three given hand gestures.

People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms-for action, imagination, and explanation. We present a computational model that captures these humanlearning abilities for a large class of simple visual concepts: handwritten characters from the world's alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several "visual Turing tests" probing the model's creative generalization abilities, which in many cases are indistinguishable from human behavior.

the human physique and the geometry of the squint angle. In this case, squint SAR has the same effect as pointing the radar 35 degrees from...for by considering the human physique and the geometry of the squint angle. In the images, the torso and the extended arms of the human are readily

It is difficult for rats to acquire daily time-place (TP) learning tasks. One theory suggests that rats do not use time of day as a stimulus signaling a specific response. In the present study, we tested rats' ability to use time of day as a discriminative stimulus. A fixed-interval procedure was used in which one lever provided reinforcement on a FI-5-s schedule in morning sessions, and the same lever provided reinforcement on a FI-30-s schedule in afternoon sessions. Because only one place was used in this paradigm, the rats could only use time of day to acquire the task. Mean responses during the first 5 s of the first trial in each session indicated that the rats did not discriminate between the two sessions. In Phase II, a different lever location was used for each of the two daily sessions, which meant that both spatial and temporal information could be used to acquire the task. The rats readily acquired the task in this phase, and probe trials indicated that the rats were using a combination of spatial and temporal information to discriminate between the two different trial types. When the spatial cue was removed in Phase III, rats no longer discriminated the two sessions, suggesting that time can only be used as a discriminative stimulus when each daily session is associated with a distinct spatial location.

Dose from radiation exposure can be estimated from dicentric chromosome (DC) frequencies in metaphase cells of peripheral blood lymphocytes. We automated DC detection by extracting features in Giemsa-stained metaphase chromosome images and classifying objects by machine learning (ML). DC detection involves (i) intensity thresholded segmentation of metaphase objects, (ii) chromosome separation by watershed transformation and elimination of inseparable chromosome clusters, fragments and staining debris using a morphological decision tree filter, (iii) determination of chromosome width and centreline, (iv) derivation of centromere candidates, and (v) distinction of DCs from monocentric chromosomes (MC) by ML. Centromere candidates are inferred from 14 image features input to a Support Vector Machine (SVM). Sixteen features derived from these candidates are then supplied to a Boosting classifier and a second SVM which determines whether a chromosome is either a DC or MC. The SVM was trained with 292 DCs and 3135 MCs, and then tested with cells exposed to either low (1 Gy) or high (2-4 Gy) radiation dose. Results were then compared with those of 3 experts. True positive rates (TPR) and positive predictive values (PPV) were determined for the tuning parameter, σ. At larger σ, PPV decreases and TPR increases. At high dose, for σ = 1.3, TPR = 0.52 and PPV = 0.83, while at σ = 1.6, the TPR = 0.65 and PPV = 0.72. At low dose and σ = 1.3, TPR = 0.67 and PPV = 0.26. The algorithm differentiates DCs from MCs, overlapped chromosomes and other objects with acceptable accuracy over a wide range of radiation exposures.

Mass spectrometry allows quantification of tens of thousands of phosphorylation sites from minute amounts of cellular material. Despite this wealth of information, our understanding of phosphorylation-based signaling is limited, in part because it is not possible to deconvolute substrate phosphorylation that is directly mediated by a particular kinase versus phosphorylation that is mediated by downstream kinases. Here, we describe a framework for assignment of direct in-vivo kinase substrates using a combination of selective chemical inhibition, quantitative phosphoproteomics, and machine learning techniques. Our workflow allows classification of phosphorylation events following inhibition of an analog-sensitive kinase into kinase-independent effects of the inhibitor, direct effects on cognate substrates and indirect effects mediated by downstream kinases or phosphatases. We applied this method to identify many direct targets of Cdc28 and Snf1 kinases in the budding yeast S. cerevisiae. Global phosphoproteome analysis of acute time-series demonstrated that dephosphorylation of direct kinase substrates occurs more rapidly compared to indirect substrates, both after inhibitor treatment and under a physiological nutrient shift in wild-type cells. Mutagenesis experiments revealed a high proportion of functionally relevant phosphorylation sites on Snf1 targets. For example, Snf1 itself was inhibited through autophosphorylation on S391 and new phosphosites were discovered that modulate the activity of the Reg1 regulatory subunit of the Glc7 phosphatase and the Gal83 β-subunit of SNF1 complex. This methodology applies to any kinase for which a functional analog sensitive version can be constructed to facilitate the dissection of the global phosphorylation network.

Might there be parallels between category learning in animals and word learning in children? To examine this possibility, we devised a new associative learning technique for teaching pigeons to sort 128 photographs of objects into 16 human language categories. We found that pigeons learned all 16 categories in parallel, they perceived the perceptual coherence of the different object categories, and they generalized their categorization behavior to novel photographs from the training categories. More detailed analyses of the factors that predict trial-by-trial learning implicated a number of factors that may shape learning. First, we found considerable trial-by-trial dependency of pigeons' categorization responses, consistent with several recent studies that invoke this dependency to claim that humans acquire words via symbolic or inferential mechanisms; this finding suggests that such dependencies may also arise in associative systems. Second, our trial-by-trial analyses divulged seemingly irrelevant aspects of the categorization task, like the spatial location of the report responses, which influenced learning. Third, those trial-by-trial analyses also supported the possibility that learning may be determined both by strengthening correct stimulus-response associations and by weakening incorrect stimulus-response associations. The parallel between all these findings and important aspects of human word learning suggests that associative learning mechanisms may play a much stronger part in complex human behavior than is commonly believed.

Long noncoding RNA (lncRNA) is a kind of noncoding RNA with length more than 200 nucleotides, which aroused interest of people in recent years. Lots of studies have confirmed that human genome contains many thousands of lncRNAs which exert great influence over some critical regulators of cellular process. With the advent of high-throughput sequencing technologies, a great quantity of sequences is waiting for exploitation. Thus, many programs are developed to distinguish differences between coding and long noncoding transcripts. Different programs are generally designed to be utilised under different circumstances and it is sensible and practical to select an appropriate method according to a certain situation. In this review, several popular methods and their advantages, disadvantages, and application scopes are summarised to assist people in employing a suitable method and obtaining a more reliable result.

This article reports on reusable mobile digital learning resources designed to assist human geography undergraduate students in exploring the geographies of life in Dublin. Developing active learning that goes beyond data collection to encourage observation and thinking in the field is important. Achieving this in the context of large class sizes…

Social learning has allowed humans to build up extensive cultural repertoires, enabling them to adapt to a wide variety of environmental and social conditions. However, it is unclear which social learning strategies people use, especially in social contexts where their payoffs depend on the

The sequencing of dance movements may be thought of as a grammar. We investigate implicit learning of regularities that govern sequences of unfamiliar, discrete dance movements. It was hypothesized that observers without prior experience with contemporary dance would be able to learn regularities that underpin structured human movement. Thirty-one…

Social learning has allowed humans to build up extensive cultural repertoires, enabling them to adapt to a wide variety of environmental and social conditions. However, it is unclear which social learning strategies people use, especially in social contexts where their payoffs depend on the behaviou

The sequencing of dance movements may be thought of as a grammar. We investigate implicit learning of regularities that govern sequences of unfamiliar, discrete dance movements. It was hypothesized that observers without prior experience with contemporary dance would be able to learn regularities that underpin structured human movement. Thirty-one…

Animal studies have shown that substantia nigra (SN) dopaminergic (DA) neurons strengthen action-reward associations during reinforcement learning, but their role in humanlearning is not known. Here, we applied microstimulation in the SN of 11 patients undergoing deep brain stimulation surgery for the treatment of Parkinson's disease as they performed a two-alternative probability learning task in which rewards were contingent on stimuli, rather than actions. Subjects demonstrated decreased learning from reward trials that were accompanied by phasic SN microstimulation compared with reward trials without stimulation. Subjects who showed large decreases in learning also showed an increased bias toward repeating actions after stimulation trials; therefore, stimulation may have decreased learning by strengthening action-reward associations rather than stimulus-reward associations. Our findings build on previous studies implicating SN DA neurons in preferentially strengthening action-reward associations during reinforcement learning.

Individuals face evolutionary trade-offs between the acquisition of costly but accurate information gained firsthand and the use of inexpensive but possibly less reliable social information. American crows (Corvus brachyrhynchos) use both sources of information to learn the facial features of a dangerous person. We exposed wild crows to a novel 'dangerous face' by wearing a unique mask as we trapped, banded and released 7-15 birds at five study sites near Seattle, WA, USA. An immediate scolding response to the dangerous mask after trapping by previously captured crows demonstrates individual learning, while an immediate response by crows that were not captured probably represents conditioning to the trapping scene by the mob of birds that assembled during the capture. Later recognition of dangerous masks by lone crows that were never captured is consistent with horizontal social learning. Independent scolding by young crows, whose parents had conditioned them to scold the dangerous mask, demonstrates vertical social learning. Crows that directly experienced trapping later discriminated among dangerous and neutral masks more precisely than did crows that learned through social means. Learning enabled scolding to double in frequency and spread at least 1.2 km from the place of origin over a 5 year period at one site.

Discrimination is a structured way of abusing people based on racial differences, hence barring them from accessing wealth, political participation and engagement in many spheres of human life. Racism and discrimination are inherently rooted in institutions in the society, the problem has spread across many social segments of the society including…

Researchers have demonstrated that discriminativelearning is facilitated when a particular outcome is associated with each relation to be learned. Our primary purpose in the two experiments reported here was to assess whether the differential outcomes procedure (DOP) would enhance 7-year-old children's learning of symbolic discriminations using three different forms of consequences in which (1) reinforcers are given when correct choices are made ("+"), (2) reinforcers are withdrawn when errors are made ("-"), or (3) children receive a reinforcer following a correct choice and lose one following an incorrect choice ("+/-"), as well as different types of reinforcers (secondary and primary reinforcers, Experiment 1; primary reinforcers alone, Experiment 2). Participants learned the task faster and showed significantly better performance whenever differential outcomes were arranged independently of (1) the way of providing consequences (+, -, or +/-) and (2) the type of reinforcers being used. Interestingly, as in a previous study with 5-year-old children (Martínez, Estévez, Fuentes, & Overmier, The Quarterly Journal of Experimental Psychology 62(8):1617-1630, 2009), the use of the DOP also enhanced long-term persistence of learning.

This analysis describes the nature of a learning organization, defines the boundaries of evidence-informed practice, identifies the elements of knowledge management, and specifies the elements of the transfer of learning. A set of principles are presented to guide managers in transforming human service organizations into learning organizations along with a set of implementation strategies that can inform participants of the values and benefits of knowledge management. This analysis features concepts and principles adapted and synthesized from research in diverse fields, such as evidence-based health care and the for-profit sector related to learning organizations, knowledge management, and the transfer of learning.

Behavioral evidence from human studies suggests that the γ-aminobutyric acid type B receptor (GABAB receptor) agonist baclofen modulates reinforcement learning and reduces craving in patients with addiction spectrum disorders. However, in contrast to the well established role of dopamine in reinforcement learning, the mechanisms by which the GABAB receptor influences reinforcement learning in humans remain completely unknown. To further elucidate this issue, a cross-over, double-blind, placebo-controlled study was performed in healthy human subjects (N=15) to test the effects of baclofen (20 and 50mg p.o.) on probabilistic reinforcement learning. Outcomes were the feedback-induced P2 component of the event-related potential, the feedback-related negativity, and the P300 component of the event-related potential. Baclofen produced a reduction of P2 amplitude over the course of the experiment, but did not modulate the feedback-related negativity. Furthermore, there was a trend towards increased learning after baclofen administration relative to placebo over the course of the experiment. The present results extend previous theories of reinforcement learning, which focus on the importance of mesolimbic dopamine signaling, and indicate that stimulation of cortical GABAB receptors in a fronto-parietal network leads to better attentional allocation in reinforcement learning. This observation is a first step in our understanding of how baclofen may improve reinforcement learning in healthy subjects. Further studies with bigger sample sizes are needed to corroborate this conclusion and furthermore, test this effect in patients with addiction spectrum disorder.

Social learning has allowed humans to build up extensive cultural repertoires, enabling them to adapt to a wide variety of environmental and social conditions. However, it is unclear which social learning strategies people use, especially in social contexts where their payoffs depend on the behaviour of others. Here we show experimentally that individuals differ in their social learning strategies and that they tend to employ the same learning strategy irrespective of the interaction context. Payoff-based learners focus on their peers' success, while decision-based learners disregard payoffs and exclusively focus on their peers' past behaviour. These individual differences may be of considerable importance for cultural evolution. By means of a simple model, we demonstrate that groups harbouring individuals with different learning strategies may be faster in adopting technological innovations and can be more efficient through successful role differentiation. Our study highlights the importance of individual variation for human interactions and sheds new light on the dynamics of cultural evolution.

A plethora of peptides are generated intracellularly, and most peptide-human leukocyte antigen (HLA)-I interactions are of a transient, unproductive nature. Without a quality control mechanism, the HLA-I system would be stressed by futile attempts to present peptides not sufficient for the stable...... according to the identity of the peptide. The facilitation was also specific for the identity of the HLA-I heavy chain, where it correlated to established tapasin dependence hierarchies. Two large sets of HLA-A*02:01 binding peptides, one extracted from natural HLA-I ligands from the SYFPEITHI database...... functionally discriminate the selected SYFPEITHI peptides from the other peptide binders with high sensitivity and specificity. We suggest that this HLA-I- and peptide-specific function, together with the functions exerted by the more C-terminal parts of tapasin, are major features of tapasin-mediated HLA...

Full Text Available Subchronic treatment with the psychotomimetic phencyclidine (PCP has been proposed as a rodent model of the negative and cognitive/executive symptoms of schizophrenia. There has, however, been a paucity of studies on this model in mice, despite the growing use of the mouse as a subject in genetic and molecular studies of schizophrenia. In the present study, we evaluated the effects of subchronic PCP treatment (5 mg/kg twice daily x 7 days, followed by 7 days withdrawal in C57BL/6J mice on 1 social behaviors using a sociability/social novelty-preference paradigm, and 2 pairwise visual discrimination and reversal learning using a touchscreen-based operant system. Results showed that mice subchronically treated with PCP made more visits to (but did not spend more time with a social stimulus relative to an inanimate one, and made more visits and spent more time investigating a novel social stimulus over a familiar one. Subchronic PCP treatment did not significantly affect behavior in either the discrimination or reversal learning tasks. These data encourage further analysis of the potential utility of mouse subchronic PCP treatment for modeling the social withdrawal component of schizophrenia. They also indicate that the treatment regimen employed was insufficient to impair our measures of discrimination and reversal learning in the C57BL/6J strain. Further work will be needed to identify alternative methods (e.g., repeated cycles of subchronic PCP treatment, use of different mouse strains that produce discrimination and/or reversal impairment, as well as other cognitive/executive measures that are sensitive to chronic PCP treatment in mice.

This paper proposes a human capabilities approach for evaluating student learning and the social and pedagogical arrangements that support equality in capabilities for all students. It outlines the focus on valuable beings and doings in the capability approach developed by Amartya Sen, and Martha Nussbaum's capabilities focus on human flourishing.…

Readers of this journal probably know how the peer review process works in the humanities disciplines and at various journals. Therefore the author explains how the law review process generally works and then what the humanities can learn and borrow from the law review process. He ends by advocating for a hybrid law review/peer review approach to…

Research has revealed that fetuses can learn from events in their environment. The most convincing evidence for fetal learning is habituation to vibroacoustic stimulation (VAS) in human fetuses and classical conditioning in rat fetuses. However, these two research areas have been independent of each other. There have been few attempts at classical…

Research has revealed that fetuses can learn from events in their environment. The most convincing evidence for fetal learning is habituation to vibroacoustic stimulation (VAS) in human fetuses and classical conditioning in rat fetuses. However, these two research areas have been independent of each other. There have been few attempts at classical…

Structural and functional MRI unveil many hidden properties of the human brain. We performed this multi-class classification study on selected subjects from the publically available attention deficit hyperactivity disorder ADHD-200 dataset of patients and healthy children. The dataset has three groups, namely, ADHD inattentive, ADHD combined, and typically developing. We calculated the global averaged functional connectivity maps across the whole cortex to extract anatomical atlas parcellation based features from the resting-state fMRI (rs-fMRI) data and cortical parcellation based features from the structural MRI (sMRI) data. In addition, the preprocessed image volumes from both of these modalities followed an ANOVA analysis separately using all the voxels. This study utilized the average measure from the most significant regions acquired from ANOVA as features for classification in addition to the multi-modal and multi-measure features of structural and functional MRI data. We extracted most discriminative features by hierarchical sparse feature elimination and selection algorithm. These features include cortical thickness, image intensity, volume, cortical thickness standard deviation, surface area, and ANOVA based features respectively. An extreme learning machine performed both the binary and multi-class classifications in comparison with support vector machines. This article reports prediction accuracy of both unimodal and multi-modal features from test data. We achieved 76.190% (p multi-class settings as well as 92.857% (p multi-modal group analysis approach with multi-measure features may improve the accuracy of the ADHD differential diagnosis.

Full Text Available Multimodal features of structural and functional magnetic resonance imaging (MRI of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001 accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.

This paper surveys recent economic research on price discrimination, both in monopoly and oligopoly markets. Topics include static and dynamic forms of price discrimination, and both final and input markets are considered. Potential antitrust aspects of price discrimination are highlighted throughout the paper. The paper argues that the informational requirements to make accurate policy are very great, and with most forms of price discrimination a laissez-faire policy may be the best availabl...

Field potentials were recorded with electrodes implanted in various cortical areas while a monkey acquired a task of go/no-go reaction time hand movement with discrimination between tone stimuli of different frequencies. After a few weeks of training, a surface-negative, depth-positive (s-N, d-P) potential (no-go potential) emerged in the dorsal bank of the principal sulcus. As the potential increased in size in 1-3 months, the monkey gradually discriminated between go and no-go stimuli. The no-go potential is considered to be related to judgement not to move and suppression of motor execution. In the superior temporal gyrus, a s-N, d-P potential at a shorter latency than the no-go potential augmented in size on both go and no-go trials, as the monkey learned the discrimination task. The s-N, d-P potential in this gyrus may reflect an information processing prior to the discrimination in the prefrontal cortex.

Full Text Available BACKGROUND: The issue of how differences in timbre are represented in the neural response still has not been well addressed, particularly with regard to the relevant brain mechanisms. Here we employ phasing and clipping of tones to produce auditory stimuli differing to describe the multidimensional nature of timbre. We investigated the auditory response and sensory gating as well, using by magnetoencephalography (MEG. METHODOLOGY/PRINCIPAL FINDINGS: Thirty-five healthy subjects without hearing deficit participated in the experiments. Two different or same tones in timbre were presented through conditioning (S1-testing (S2 paradigm as a pair with an interval of 500 ms. As a result, the magnitudes of auditory M50 and M100 responses were different with timbre in both hemispheres. This result might support that timbre, at least by phasing and clipping, is discriminated in the auditory early processing. The second response in a pair affected by S1 in the consecutive stimuli occurred in M100 of the left hemisphere, whereas both M50 and M100 responses to S2 only in the right hemisphere reflected whether two stimuli in a pair were the same or not. Both M50 and M100 magnitudes were different with the presenting order (S1 vs. S2 for both same and different conditions in the both hemispheres. CONCLUSIONS/SIGNIFICANCES: Our results demonstrate that the auditory response depends on timbre characteristics. Moreover, it was revealed that the auditory sensory gating is determined not by the stimulus that directly evokes the response, but rather by whether or not the two stimuli are identical in timbre.

Influential neurocomputational models emphasize dopamine (DA) as an electrophysiological and neurochemical correlate of reinforcement learning. However, evidence of a specific causal role of DA receptors in learning has been less forthcoming, especially in humans. Here we combine, in a between-subjects design, administration of a high dose of the selective DA D2/3-receptor antagonist sulpiride with genetic analysis of the DA D2 receptor in a behavioral study of reinforcement learning in a sample of 78 healthy male volunteers. In contrast to predictions of prevailing models emphasizing DA's pivotal role in learning via prediction errors, we found that sulpiride did not disrupt learning, but rather induced profound impairments in choice performance. The disruption was selective for stimuli indicating reward, whereas loss avoidance performance was unaffected. Effects were driven by volunteers with higher serum levels of the drug, and in those with genetically determined lower density of striatal DA D2 receptors. This is the clearest demonstration to date for a causal modulatory role of the DA D2 receptor in choice performance that might be distinct from learning. Our findings challenge current reward prediction error models of reinforcement learning, and suggest that classical animal models emphasizing a role of postsynaptic DA D2 receptors in motivational aspects of reinforcement learning may apply to humans as well.

Similarity learning has always been a popular topic in computer vision research. Among this, facial similarity is especially important and diﬃcult due to its wide applications and the nonrigid nature of human faces. The large gap between feature representations and human perceptual descriptions makes the problem even harder. In this paper, we learn facial similarity through human-computer interactions. To learn perceptual similarities of faces in a gallery set, we ask users to label some candidate images with their similarities to a probe image. Based on users’ responses, a sampling algorithm actively generates a probe image and a set of candidates for the next query. Assisted with human efforts, the algorithm embeds all the images into a space where the distance between two subjects conforms to their dissimilarity in human perception. We apply the learned embedding to face retrieval and compare our method with some feature-based methods on a dataset we collect from social network sites (SNS). Experimental results demonstrate that incorporating human efforts can ensure retrieval accuracy. At the same time, the active sampling algorithm reduces human efforts.

AIM: For the first time, extracts obtained from human plasma samples by electromembrane extraction (EME) were investigated comprehensively with particular respect to phospholipids using ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). Thhe purpose was to invest...

Recent evidence suggests that adults selectively attend to features of action, such as how a hand contacts an object, and less to configural properties of action, such as spatial trajectory, when observing human actions. The current research investigated whether this bias develops in infancy. We utilized a habituation paradigm to assess…

How do social interactions form and modulate the neural representations of specific complex signals? This question can be addressed in the songbird auditory system. Like humans, songbirds learn to vocalize by imitating tutors heard during development. These learned vocalizations are important in reproductive and social interactions and in individual recognition. As a model for the social reinforcement of particular songs, male zebra finches were trained to peck for a food reward in response to one song stimulus (GO) and to withhold responding for another (NoGO). After performance reached criterion, single and multiunit neural responses to both trained and novel stimuli were obtained from multiple electrodes inserted bilaterally into two songbird auditory processing areas [caudomedial mesopallium (CMM) and caudomedial nidopallium (NCM)] of awake, restrained birds. Neurons in these areas undergo stimulus-specific adaptation to repeated song stimuli, and responses to familiar stimuli adapt more slowly than to novel stimuli. The results show that auditory responses differed in NCM and CMM for trained (GO and NoGO) stimuli vs. novel song stimuli. When subjects were grouped by the number of training days required to reach criterion, fast learners showed larger neural responses and faster stimulus-specific adaptation to all stimuli than slow learners in both areas. Furthermore, responses in NCM of fast learners were more strongly left-lateralized than in slow learners. Thus auditory responses in these sensory areas not only encode stimulus familiarity, but also reflect behavioral reinforcement in our paradigm, and can potentially be modulated by social interactions.

Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours). Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns. Discernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 ± 8.8% and 86.3 ± 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales. Discernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over time needs to be reconsidered in the context of

From early childhood, human beings learn not only from collections of facts about the world but also from social contexts through observations of other people, communication, and explicit teaching. In these contexts, the data are the result of human actions-actions that come about because of people's goals and intentions. To interpret the implications of others' actions correctly, learners must understand the people generating the data. Most models of learning, however, assume that data are randomly collected facts about the world and cannot explain how social contexts influence learning. We provide a Bayesian analysis of learning from knowledgeable others, which formalizes how learners may use a person's actions and goals to make inferences about the actor's knowledge about the world. We illustrate this framework using two examples from causal learning and conclude by discussing the implications for cognition, social reasoning, and cognitive development.

In this article, I discuss structural discrimination, an underrepresented area of study in Danish discrimination and intercultural research. It is defined here as discursive and constitutive, and presented as a central element of my analytical approach. This notion is employed in the with which...... to understand and identify aspects of power and asymmetry in communication and interactions. With this as a defining term, I address how exclusion and discrimination exist, while also being indiscernible, within widely accepted societal norms. I introduce the concepts of microdiscrimination and benevolent...... discrimination as two ways of articulating particular, opaque forms of racial discrimination that occur in everyday Danish (and other) contexts, and have therefore become normalized. I present and discuss discrimination as it surfaces in data from my empirical studies of discrimination in Danish contexts...

This paper presents an experimental study to investigate the learning and decision making behavior of individuals in a human society. Social learning is used as the mathematical basis for modelling interaction of individuals that aim to perform a perceptual task interactively. A psychology experiment was conducted on a group of undergraduate students at the University of British Columbia to examine whether the decision (action) of one individual affects the decision of the subsequent individu...

The University of Porto, like other universities around the world, is working to promote effective integration of various learning techniques. This paper describes the results of a research that aimed to find and test new technologies in TL of human nutrition in a second-cycle course of Porto University. The application of blended-learning as a strategy to respond to the numerous pedagogical challenges that Bologna presents to Higher Education and its use to join what should not be separated:...

Purpose: This paper aims to describe an attempt to develop a more effective technique to teach self-awareness and relationship skills. Design/methodology/approach: A journal is used in combination with a model of human nature. The model lists human characteristics that the management trainee must identify in themselves and others they interact…

Collaboration has become increasingly widespread in the software industry as systems have become larger and more complex, adding human complexity to the technological complexity already involved in developing software systems. To deal with this complexity, human-centric software development methods, such as Extreme Programming and other agile…

Collaboration has become increasingly widespread in the software industry as systems have become larger and more complex, adding human complexity to the technological complexity already involved in developing software systems. To deal with this complexity, human-centric software development methods, such as Extreme Programming and other agile…

To the best of our knowledge, the genetic foundations that guide human brain development have not changed fundamentally during the past 50,000 years. However, because of their cognitive potential, humans have changed the world tremendously in the past centuries. They have invented technical devices, institutions that regulate cooperation and competition, and symbol systems, such as script and mathematics, that serve as reasoning tools. The exceptional learning ability of humans allows newborns to adapt to the world they are born into; however, there are tremendous individual differences in learning ability among humans that become obvious in school at the latest. Cognitive psychology has developed models of memory and information processing that attempt to explain how humanslearn (general perspective), while the variation among individuals (differential perspective) has been the focus of psychometric intelligence research. Although both lines of research have been proceeding independently, they increasingly converge, as both investigate the concepts of working memory and knowledge construction. This review begins with presenting state-of-the-art research on human information processing and its potential in academic learning. Then, a brief overview of the history of psychometric intelligence research is combined with presenting recent work on the role of intelligence in modern societies and on the nature-nurture debate. Finally, promising approaches to integrating the general and differential perspective will be discussed in the conclusion of this review.

The Sleeping Beauty (SB) transposon mutagenesis screen is a powerful tool to facilitate the discovery of cancer genes that drive tumorigenesis in mouse models. In this study, we sought to identify genes that functionally cooperate with sonic hedgehog signaling to initiate medulloblastoma (MB), a tumor of the cerebellum. By combining SB mutagenesis with Patched1 heterozygous mice (Ptch1lacZ/+), we observed an increased frequency of MB and decreased tumor-free survival compared with Ptch1lacZ/+ controls. From an analysis of 85 tumors, we identified 77 common insertion sites that map to 56 genes potentially driving increased tumorigenesis. The common insertion site genes identified in the mutagenesis screen were mapped to human orthologs, which were used to select probes and corresponding expression data from an independent set of previously described human MB samples, and surprisingly were capable of accurately clustering known molecular subgroups of MB, thereby defining common regulatory networks underlying all forms of MB irrespective of subgroup. We performed a network analysis to discover the likely mechanisms of action of subnetworks and used an in vivo model to confirm a role for a highly ranked candidate gene, Nfia, in promoting MB formation. Our analysis implicates candidate cancer genes in the deregulation of apoptosis and translational elongation, and reveals a strong signature of transcriptional regulation that will have broad impact on expression programs in MB. These networks provide functional insights into the complex biology of human MB and identify potential avenues for intervention common to all clinical subgroups. PMID:24167280

Two problems are addressed in this paper (i) the fluorescent marker-based and the (ii) marker-free discrimination between healthy and cancerous human tissues. For both applications the performance of hyper-spectral methods are quantified. Fluorescent marker-based tissue classification uses a number of fluorescent markers to dye specific parts of a human cell. The challenge is that the emission spectra of the fluorescent dyes overlap considerably. They are, furthermore disturbed by the inherent auto-fluorescence of human tissue. This results in ambiguities and decreased image contrast causing difficulties for the treatment decision. The higher spectral resolution introduced by tunable-filter-based spectral imaging in combination with spectral unmixing techniques results in an improvement of the image contrast and therefore more reliable information for the physician to choose the treatment decision. Marker-free tissue classification is based solely on the subtle spectral features of human tissue without the use of artificial markers. The challenge in this case is that the spectral differences between healthy and cancerous tissues are subtle and embedded in intra- and inter-patient variations of these features. The contributions of this paper are (i) the evaluation of hyper-spectral imaging in combination with spectral unmixing techniques for fluorescence marker-based tissue classification, (ii) the evaluation of spectral imaging for marker-free intra surgery tissue classification. Within this paper, we consider real hyper-spectral fluorescence and endoscopy data sets to emphasize the practical capability of the proposed methods. It is shown that the combination of spectral imaging with multivariate statistical methods can improve the sensitivity and specificity of the detection and the staging of cancerous tissues compared to standard procedures.

Competitive learning is an important approach for clustering analysis. The rival penalized competitive learning ( RPCL) algorithm has the ability of selecting the correct number of clusters automatically, but its performance is sensitive to the selection of learning rate and de-learning rate. In fact, it is unreasonable that all the rival units are treated as redundant units to be penalized in the variant algorithm called rival penalization controlled competitive learning ( RPCCL) . In this paper, a discriminative rival penalization controlled competitive learning ( DRPCCL) is presented. The learning rate of winning units adaptively adjusts during iteration in the proposed method. Meanwhile, a discriminative penalization controlled mechanism is used to discriminate the redundant units and the correct units in the rival units. The correct units and redundant units are given a slight penalization and a heavier penalization respectively, which makes this algorithm get exact number of clusters and reasonable centre of clusters. The experimental result demonstrates that compared with RPCL and RPCCL, DRPCCL achieves more accurate performance.%竞争学习在聚类分析中是一种重要的学习方式，次胜者惩罚竞争学习( RPCL)算法虽能自动选择合理的类别数，但其性能对学习率和惩罚率的取值较敏感，其变种惩罚控制竞争学习( RPCCL)算法将所有的竞争单元当成冗余单元进行惩罚也不合理。文中提出一种可区分惩罚控制竞争学习算法( DRPCCL)。算法中获胜单元的学习率会在迭代过程中自适应调整。同时该算法使用一种可区分惩罚控制机制来区分竞争单元中的冗余单元和正确单元，给予冗余单元较重惩罚，正确单元轻微惩罚，使得算法能自动确定正确类别数和中心点位置。最后通过实验对比分析证明DRPCCL算法的聚类效果比RPCL算法和RPCCL算法更准确。

Full Text Available In human causal learning, excitatory and inhibitory learning effects can sometimes be found in the same paradigm by altering the learning conditions. This study aims to explore whether learning in the feature negative paradigm can be dissociated by emphasising speed over accuracy. In two causal learning experiments, participants were given a feature negative discrimination in which the outcome caused by one cue was prevented by the addition of another. Participants completed training trials either in a self-paced fashion with instructions emphasising accuracy, or under strict time constraints with instructions emphasising speed. Using summation tests in which the preventative cue was paired with another causal cue, participants in the accuracy groups correctly rated the preventative cue as if it reduced the probability of the outcome. However, participants in the speed groups rated the preventative cue as if it increased the probability of the outcome. In Experiment 1, both speed and accuracy groups later judged the same cue to be preventative in a reasoned inference task. Experiment 2 failed to find evidence of similar dissociations in retrospective revaluation (release from overshadowing vs. mediated extinction or learning about a redundant cue (blocking vs. augmentation. However in the same experiment, the tendency for the accuracy group to show conditioned inhibition and the speed group to show second-order conditioning was consistent even across sub-sets of the speed and accuracy groups with equivalent accuracy in training, suggesting that second-order conditioning is not merely a consequence of poorer acquisition. This dissociation mirrors the trade-off between second-order conditioning and conditioned inhibition observed in animal conditioning when training is extended.

Detection of athletes who use synthetic human growth hormone (hGH; or somatotropin) to enhance physical strength and obtain an advantage in competitive sports is a formidable problem, as rhGH is virtually identical to the natural pituitary hormone. However, some post-translational and other modifications have been documented by chromatographic separation and mass spectrometry (MS) in a small percentage of rhGH. In the present work, development of DNA aptamers against research-grade rhGH and n...

JIAO Xun is the strongest supporter of theory of goodness of human nature proposed by Menci in Qing Dynasty and he emphasizes“human nature, born like that”. The basic ideas of his theory of human nature include“wise, human beings; unwise, animals”, and therefore human beings are born good while animals are born bad. JIAO Xun holds that man has“four instincts towards goodness”, that is, man can learn, man can be taught wise, man has feelings and desires to be re-fined, man can make“desire”be realized naturally and necessarily, man knows to respect and learn, man knows right and changes accordingly, man can“learn accordingly”feelings and desires so man is good. JIAO Xun admits human nature is the basic quality to be born with and through comparing man and animals he proves that the social attribute of man to possess the“four in-stincts”is good and it can be proved by civilization and wisdom.%焦循是清代对孟子性善说申发最用力者，他强调“性，生而然者也”，其人性论立论基点是“智，人也；不智，禽兽也”，故人性善，禽兽之性不善。焦循认为，人具“四端”，人能知，人可教而明，人知情有欲求精妍，人可使“欲”从自然达至必然，人知尊贤采善，人知权善变，人可“旁通”情欲，故人性善。焦循在承认人性是本然之性、生而有之的前提之下，通过人兽对比，着力证明人具“四端”的社会属性是善的，以文明、智慧力证人性善。

Detection of athletes who use synthetic human growth hormone (hGH; or somatotropin) to enhance physical strength and obtain an advantage in competitive sports is a formidable problem, as rhGH is virtually identical to the natural pituitary hormone. However, some post-translational and other modifications have been documented by chromatographic separation and mass spectrometry (MS) in a small percentage of rhGH. In the present work, development of DNA aptamers against research-grade rhGH and natural hGH with adsorption of the rhGH aptamers against natural hGH was shown to produce a small family of aptamer sequences that bound consistently with greater affinity to rhGH over a low nanogram-to-microgram range in ELISA-like microplate assays. This collection of rhGH discriminatory aptamer sequences shared some short sequence segments and secondary structural features. The top rhGH discriminatory aptamers also appeared to cross-react with human myoglobin and BSA but not with bone collagen peptides and an unrelated viral envelope peptide. The cross-reactivity results suggested several strings of up to five consecutive amino acids that might serve as common epitopes for aptamer binding. SDS-PAGE revealed that the rhGH existed largely as a 45-kDa dimer, and the natural hGH was almost exclusively monomeric. The existence of the rhGH dimer suggests that a discontinuous "bridge" epitope may exist on the rhGH, which spans the subunits, thereby accounting somewhat for the difference in detection. Overall, these results suggest that aptamers might be useful for routine, presumptive laboratory screening to identify athletes who are potentially cheating by administration of rhGH.

A holistic, collaborative interprofessional team approach, which includes patients and families as significant decision-making members, has been proposed to address the increasing burden being placed on the health-care system. This project hypothesized that learning activities related to the humanities during clinical placements could enhance interprofessional teamwork. Through an interprofessional team of faculty, clinical staff, students, and patient representatives, we developed and piloted the self-learning module, "interprofessional education for collaborative person-centred practice through the humanities". The module was designed to provide learners from different professions and educational levels with a clinical placement/residency experience that would enable them, through a lens of the humanities, to better understand interprofessional collaborative person-centred care without structured interprofessional placement activities. Learners reported the self-paced and self-directed module to be a satisfactory learning experience in all four areas of care at our institution, and certain attitudes and knowledge were significantly and positively affected. The module's evaluation resulted in a revised edition providing improved structure and instruction for students with no experience in self-directed learning. The module was recently adapted into an interactive bilingual (French and English) online e-learning module to facilitate its integration into the pre-licensure curriculum at colleges and universities.

Public high school students with deafness vividly learned about the realities of discrimination when they were informed of "new rules for deaf students," which required that they wear "deaf badges" in school, follow a strict dress code, and so on. After the "new rules" hoax was revealed, students' feelings and reactions to the situation were…

Wisdom is a complex phenomenon: it finds its home primarily but not exclusively in theology, philosophy, psychology, education--that is, in the humanities--and in life itself. In a paradoxical manner, wisdom finds its home in the world of the unanswerable, where there are no empirical proofs and no obvious answers. Wisdom actually finds its place…

Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.

This study investigated the attitudes of Prospective Human Resource Personnel toward degrees obtained by distance learning in comparison to those obtained through conventional degree program. Using a cross-sectional survey design, a total of 215 postgraduate students who had been or had potential to be involved in the hiring process in their…

The study presented in this paper is a research synthesis examining how issues relating to the teaching and learning of children's human rights have been approached in educational research. Drawing theoretically on the European Didaktik tradition, the purpose of the paper is to map and synthesise the educational interest in children's rights…

This article contrasts the view of lifelong learning posed by the human capital discourse with Freire's understanding of education as a lifelong journey toward personal growth and social transformation. Rather than reducing learners to objects of economic globalization, Freire's pedagogy considers students as political participants who actively…

Learning from rewards generated by a human trainer observing an agent in action has proven to be a powerful method for non-experts in autonomous agents to teach such agents to perform challenging tasks. Since the efficacy of this approach depends critically on the reward the trainer provides, we con

The present experiments assessed the effects of different manipulations between cue preexposure and cue-outcome pairings on latent inhibition (LI) in a predictive learning task with human participants. To facilitate LI, preexposure and acquisition with the target cues took place while participants performed a secondary task. Presentation of…

This study investigates the acquisition of integrated object manipulation and categorization abilities through a series of experiments in which human adults and artificial agents were asked to learn to manipulate two-dimensional objects that varied in shape, color, weight, and color intensity. The analysis of the obtained results and the…

This study investigated the attitudes of Prospective Human Resource Personnel toward degrees obtained by distance learning in comparison to those obtained through conventional degree program. Using a cross-sectional survey design, a total of 215 postgraduate students who had been or had potential to be involved in the hiring process in their…

Full Text Available Selenium is an essential trace element incorporated into selenoproteins as selenocysteine. Selenocysteine (Sec lyases (SCLs and cysteine (Cys desulfurases (CDs catalyze the removal of selenium or sulfur from Sec or Cys, respectively, and generally accept both substrates. Intriguingly, human SCL (hSCL is specific for Sec even though the only difference between Sec and Cys is a single chalcogen atom.The crystal structure of hSCL was recently determined and gain-of-function protein variants that also could accept Cys as substrate were identified. To obtain mechanistic insight into the chemical basis for its substrate discrimination, we here report time-resolved spectroscopic studies comparing the reactions of the Sec-specific wild-type hSCL and the gain-of-function D146K/H389T variant, when given Cys as a substrate. The data are interpreted in light of other studies of SCL/CD enzymes and offer mechanistic insight into the function of the wild-type enzyme. Based on these results and previously available data we propose a reaction mechanism whereby the Sec over Cys specificity is achieved using a combination of chemical and physico-mechanical control mechanisms.

Selenium is an essential trace element incorporated into selenoproteins as selenocysteine. Selenocysteine (Sec) lyases (SCLs) and cysteine (Cys) desulfurases (CDs) catalyze the removal of selenium or sulfur from Sec or Cys, respectively, and generally accept both substrates. Intriguingly, human SCL (hSCL) is specific for Sec even though the only difference between Sec and Cys is a single chalcogen atom.The crystal structure of hSCL was recently determined and gain-of-function protein variants that also could accept Cys as substrate were identified. To obtain mechanistic insight into the chemical basis for its substrate discrimination, we here report time-resolved spectroscopic studies comparing the reactions of the Sec-specific wild-type hSCL and the gain-of-function D146K/H389T variant, when given Cys as a substrate. The data are interpreted in light of other studies of SCL/CD enzymes and offer mechanistic insight into the function of the wild-type enzyme. Based on these results and previously available data we propose a reaction mechanism whereby the Sec over Cys specificity is achieved using a combination of chemical and physico-mechanical control mechanisms.

REV1 is a member of the Y-family DNA polymerases, but is atypical in utilizing only dCTP with a preference for guanine (G) as the template. Crystallography of the REV1-DNA-dCTP ternary complex has revealed a unique mechanism by which template G is evicted from the DNA helix and incoming dCTP is recognized by an arginine residue in an alpha-loop, termed the N-digit. To better understand functions of its individual amino acid residues, we made a series of mutant human REV1 proteins. We found that R357 and L358 play vital roles in template binding. Furthermore, extensive mutation analysis revealed a novel function of R357 for substrate discrimination, in addition to previously proposed specific interaction with incoming dCTP. We found that the binding pocket for dCTP of REV1 has also significant but latent affinity for dGTP. The results suggest that the positive charge on R357 could prevent interaction with dGTP. We propose that both direct and indirect mechanisms mediated by R357 ensure specificity for dCTP.

Every student has the right to an education free from discrimination that provides high-quality, equitable opportunities to learn. Unfortunately, sometimes individuals or systems may act in ways that violate this right. Discrimination occurs when people are treated unequally or less favorably than others because of some real or perceived…

A discriminant manifold learning approach for hyperspectral image dimension reduction was proposed. In order to overcome the high dimensional and high redundancy of remotely sensed earth observation images, a modified manifold learning algorithm was suggested for dataset linear dimensional reduction to improve the performance of image classification. The proposed method addressed the discriminative information of given training samples into the current manifold learning framework to learn an optimal subspace for subsequent classification, in particular, the linearization of discriminant manifold learning is introduced to deal with the out of sample problem. Experiments on hyperspectral image demonstrated that the proposed method could achieve higher classification rate than the conventional image classification technologies.%本文提出了一种高光谱图像降维的判别流形学习方法.针对获取的大量遥感对地观测数据存在大量冗余信息的特点,引入改进的流形学习方法对高光谱遥感数据进行降维处理,以提高遥感图像自动分类的总体准确度.该方法充分利用遥感图像自动分类中训练样本的判别信息,将输入样本的类别信息加入到常规流形学习方法的框架中,从本质上提高输出的特征在低维空间中的判别力.同时,引入线性化模型以解决流形学习方法中常见的小样本问题.对高光谱遥感图像自动分类的实验表明,基于判别流形学习的高光谱遥感图像自动分类方法能够显著地提高图像分类准确度.

Research in decision-making has focused on the role of dopamine and its striatal targets in guiding choices via learned stimulus-reward or stimulus-response associations, behavior that is well described by reinforcement learning theories. However, basic reinforcement learning is relatively limited in scope and does not explain how learning about stimulus regularities or relations may guide decision-making. A candidate mechanism for this type of learning comes from the domain of memory, which has highlighted a role for the hippocampus in learning of stimulus-stimulus relations, typically dissociated from the role of the striatum in stimulus-response learning. Here, we used functional magnetic resonance imaging and computational model-based analyses to examine the joint contributions of these mechanisms to reinforcement learning. Humans performed a reinforcement learning task with added relational structure, modeled after tasks used to isolate hippocampal contributions to memory. On each trial participants chose one of four options, but the reward probabilities for pairs of options were correlated across trials. This (uninstructed) relationship between pairs of options potentially enabled an observer to learn about option values based on experience with the other options and to generalize across them. We observed blood oxygen level-dependent (BOLD) activity related to learning in the striatum and also in the hippocampus. By comparing a basic reinforcement learning model to one augmented to allow feedback to generalize between correlated options, we tested whether choice behavior and BOLD activity were influenced by the opportunity to generalize across correlated options. Although such generalization goes beyond standard computational accounts of reinforcement learning and striatal BOLD, both choices and striatal BOLD activity were better explained by the augmented model. Consistent with the hypothesized role for the hippocampus in this generalization, functional

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

This study aimed to implement innovative teaching methods--blended learning strategies--that include the use of new information technologies in the teaching of human anatomy and to analyse both the impact of these strategies on academic performance, and the degree of user satisfaction. The study was carried out among students in Year 1 of the biology degree curriculum (human biology profile) at Pompeu Fabra University, Barcelona. Two groups of students were tested on knowledge of the anatomy of the locomotor system and results compared between groups. Blended learning strategies were employed in 1 group (BL group, n = 69); the other (TT group; n = 65) received traditional teaching aided by complementary material that could be accessed on the Internet. Both groups were evaluated using the same types of examination. The average marks presented statistically significant differences (BL 6.3 versus TT 5.0; P teaching received. Blended learning was more effective than traditional teaching for teaching human anatomy.

We taught 4 participants with autism to discriminate between the reinforced and nonreinforced responses of an adult model and evaluated the effectiveness of this intervention using a multiple baseline design. During baseline, participants were simply exposed to adult models' correct and incorrect responses and the respective consequences of each.…

Full Text Available Previous reports suggest that pigeons are highly sensitive to speed of motion (Cook, Beale, Koban, 2011; Herbranson, Fremouw, & Shimp, 2002. In contrast, we found that pigeons required more extensive training to learn speed discrimination than size discrimination (Lazareva, Young, & Wasserman, 2014. However, our results were based on a comparison of two experiments conducted in different laboratories, complicating the interpretation of the data. Here, we trained pigeons to perform size discrimination or speed discrimination in a two-alternative simultaneous discrimination task using a within-subject design. All birds acquired size discrimination much faster than speed discrimination, confirming our prior report. We further explored pigeons’ sensitivity to differences in speed and size by training them to discriminate two end-point stimuli in a two-alternative forced-choice task and then presenting a wide range of testing stimuli located between the training end-point stimuli. The results again indicated weaker control by the differences in speed in comparison to size; comparable results were obtained for human participants.

Full Text Available Studies of the neural mechanisms of navigation and context discrimination have generated a powerful heuristic for understanding how neural codes, circuits and computations contribute to accurate behavior as animals traverse and learn about spatially-extended environments. It is assumed that memories are updated as a result of spatial experience. The mechanism, however, for such a process is not clear. Here we suggest that one revealing approach to study this issue is to integrate our knowledge about limbic system mediated navigation and context discrimination with knowledge about how midbrain neural circuitry mediates decision making. This perspective should lead to new and specific neural theories about how choices that we make during navigation determine what information is ultimately learned and remembered. This same circuitry may be involved when past experiences come to bias future spatial perceptions and response selection. With old age come not only important changes in limbic system operations, but also significant decline in the function of midbrain regions that underlie accurate and efficient decisions. Thus suboptimal accuracy of spatial context-based decision making may be at least in part responsible for the common observation of spatial memory decline in old age.

The interactive partially observable Markov decision process (I-POMDP) is a recently developed framework which extends the POMDP to the multi-agent setting by including agent models in the state space. This paper argues for formulating the problem of an agent learning interactively from a human teacher as an I-POMDP, where the agent \\emph{programming} to be learned is captured by random variables in the agent's state space, all \\emph{signals} from the human teacher are treated as observed random variables, and the human teacher, modeled as a distinct agent, is explicitly represented in the agent's state space. The main benefits of this approach are: i. a principled action selection mechanism, ii. a principled belief update mechanism, iii. support for the most common teacher \\emph{signals}, and iv. the anticipated production of complex beneficial interactions. The proposed formulation, its benefits, and several open questions are presented.

Full Text Available Background Learned irrelevance (LIRR represents one of the mechanisms of attentional set-shifting and refers to the inability to attend to, or to learn about, any aspect of a stimulus previously experienced as irrelevant. Although it has been extensively studied in the context of clinical populations, not much is known about LIRR effects in relation to normal variation in individual differences. The present study was designed to assess how temperamental factors may modulate LIRR. Participants and procedures Sixty-eight healthy volunteers performed a visual discriminationlearning task modelled after Wisconsin Card Sorting Test. To test the susceptibility to learned irrelevance, participants were expected to shift their attention either to a dimension that prior to the extra-dimensional shift was completely irrelevant, or to a dimension that was previously partly correlated with reinforcement. Temperamental traits were assessed using the Formal Characteristics of Behaviour-Temperament Inventory (Zawadzki & Strelau, 1997. Intelligence level was stratified according to Raven’s Advanced Progressive Matrices (Raven, Raven, & Court, 2003. Results Low level of Briskness and high level of Perseverance were related to enhanced susceptibility to LIRR. High levels of Activity and Emotional Reactivity were related to the poorer performance on the extra-dimensional set-shifting. No effects of other temperament characteristics or intelligence on LIRR were observed. Conclusions The results confirm a strong variation in LIRR related to individual differences in temperament, which appears to be unrelated to DA function. Our results highlight the importance of considering individual differences in studies on cognitive control.

Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people’s adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics.

Much has been revealed concerning human motor learning at the behavioral level [1, 2], but less is known about changes in the involved neural circuits and signals. By examining muscle spindle responses during a classic visuomotor adaptation task [3-6] performed by fully alert humans, I found substantial modulation of sensory afferent signals as a function of adaptation state. Specifically, spindle control was independent of concurrent muscle activity but was specific to movement direction (representing muscle lengthening versus shortening) and to different stages of learning. Increased spindle afferent responses to muscle stretch occurring early during learning reflected individual error size and were negatively related to subsequent antagonist activity (i.e., 60-80 ms thereafter). Relative increases in tonic afferent output early during learning were predictive of the subjects' adaptation rate. I also found that independent spindle control during sensory realignment (the "washout" stage) induced afferent signal "linearization" with respect to muscle length (i.e., signals were more tuned to hand position). The results demonstrate for the first time that motor learning also involves independent and state-related modulation of sensory mechanoreceptor signals. The current findings suggest that adaptive motor performance also relies on the independent control of sensors, not just of muscles. I propose that the "γ" motor system innervating spindles acts to facilitate the acquisition and extraction of task-relevant information at the early stages of sensorimotor adaptation. This designates a more active and targeted role for the human proprioceptive system during motor learning.

Full Text Available Problem statement: With the advent of globalization and information and communication technology (ICT, web-facilitated learning strategy has taken an important role in the learning and teaching process. This paper examines how bioethics principles on human dignity and human rights can be learned through web-facilitated learning strategies among tertiary level International Relations students. Bioethics is an emerging field that concerns states and inter-state relations. It is about thinking globally about ethics and about our moral judgment about life, the environment and other species. The objective of this study is to provide an assessment on how graduate students of International Relations use web-based tools to gather information about global bioethics principles. Approach: The research data is collected through feedbacks solicited from some 40 post-graduate students of International Relations on (i self-assessment on the learning acquired regarding the bioethics principles using web resources and (ii through a set of pre- and post-tests to test the knowledge acquired on the subject matter. Results: The findings reveal that through the use of web-facilitated learning strategy respondents showed increased comprehension and receptiveness towards bioethics principles on human dignity and human rights. Conclusion: Therefore the study concludes that the use of web-facilitated learning strategy can emphasize the importance of bioethics principles in understanding the ethical framework in dealing with human dignity and human rights. The research findings may provide useful information for scholars and researchers developing teaching strategies using bioethics resources.

Full Text Available Kinderumwelt – an agency of the German pediatricians concerned with environmental medicine – has developed an e-learning module about human-biomonitoring. It allows active learning because the user can study interactively with the help of a model. The HBM module is part of www.allum.de (the internet portal „Allergy, Environment, Health” and is available in German and English. Since the module works mainly with images, it is also accessible to users with incomplete command of the English or German languages.

Full Text Available Background: From the moment scientists identified Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome (HIV/AIDS, social responses of fear, denial, stigma, and discrimination have accompanied the epidemic. Aims: To assess the rate of disclosure of HIV serostatus, reactions by the HIV/AIDS patients and their spouse, and discrimination faced by the patients. Methods: The present cross-sectional study was conducted at Antiretroviral Therapy (ART center of a rural tertiary care hospital, situated in Marathawada region of Maharashtra state from November 2008 to October 2010. Totally, 801 HIV-positive patients coming to ART center for treatment were included after ensuring confidentiality and taking informed consent. A preformed questionnaire was used to enquire about reaction after diagnosis, disclosure, and discrimination faced by the patients. The data analyzed using descriptive statistics and Chi-square test. Results: The most common immediate reaction by the HIV patients after getting diagnosed as seropositive was fear (593, 74.03% followed by depression (385, 48.06% and suicidal thoughts (98, 12.25%. Out of 801 patients, 769 (96% had spouse and of these maximum number of patients (653, 84.92% had disclosed HIV status to their spouses. Most common immediate reaction by spouse after disclosure was crime (324, 42.13% followed by horror (294, 38.23% and anger (237, 36.29%. Maximum number of patients were discriminated by friends (120, 71.01% followed by discrimination at workplace (49, 67.12%, by neighbors (32, 56.14%, and by relatives (53, 43.80%. Conclusion: Male positives were granted greater acceptance, care, and support by their spouses. More percentage of females discriminated by neighbors, relatives, and friends and at workplace which might be due to factors like customs, morals, and taboos.

Discriminant analysis (DA) has previously been shown to allow the proposal of simple guidelines for the classification of 73 chemical enhancers of percutaneous absorption. Pugh et al. employed DA to classify such enhancers into simple categories, based on the physicochemical properties of the enhancer molecules (Pugh et al., 2005). While this approach provided a reasonable accuracy of classification it was unable to provide a consistently reliable estimate of enhancement ratio (ER, defined as the amount of hydrocortisone transferred after 24h, relative to control). Machine Learning methods, including Gaussian process (GP) regression, have recently been employed in the prediction of percutaneous absorption of exogenous chemicals (Moss et al., 2009; Lam et al., 2010; Sun et al., 2011). They have shown that they provide more accurate predictions of these phenomena. In this study several Machine Learning methods, including the K-nearest-neighbour (KNN) regression, single layer networks, radial basis function networks and the SVM classifier were applied to an enhancer dataset reported previously. The SMOTE sampling method was used to oversample chemical compounds with ER>10 in each training set in order to improve estimation of GP and KNN. Results show that models using five physicochemical descriptors exhibit better performance than those with three features. The best classification result was obtained by using the SVM method without dealing with imbalanced data. Following over-sampling, GP gives the best result. It correctly assigned 8 of the 12 "good" (ER>10) enhancers and 56 of the 59 "poor" enhancers (ERMachine Learning methods are that they can provide more accurate classification of enhancer type with fewer false-positive results and that, unlike discriminant analysis, they are able to make predictions of enhancer ability.

Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward

NASA's Constellation program provided a unique testbed for Human Systems Integration (HSI) as a fundamental element of the Systems Engineering process. Constellation was the first major program to have HSI mandated by NASA's Human Rating document. Proper HSI is critical to the success of any project that relies on humans to function as operators, maintainers, or controllers of a system. HSI improves mission, system and human performance, significantly reduces lifecycle costs, lowers risk and minimizes re-design. Successful HSI begins with sufficient project schedule dedicated to the generation of human systems requirements, but is by no means solely a requirements management process. A top-down systems engineering process that recognizes throughout the organization, human factors as a technical discipline equal to traditional engineering disciplines with authority for the overall system. This partners with a bottoms-up mechanism for human-centered design and technical issue resolution. The Constellation Human Systems Integration Group (HSIG) was a part of the Systems Engineering and Integration (SE&I) organization within the program office, and existed alongside similar groups such as Flight Performance, Environments & Constraints, and Integrated Loads, Structures and Mechanisms. While the HSIG successfully managed, via influence leadership, a down-and-in Community of Practice to facilitate technical integration and issue resolution, it lacked parallel top-down authority to drive integrated design. This presentation will discuss how HSI was applied to Constellation, the lessons learned and best practices it revealed, and recommendations to future NASA program and project managers. This presentation will discuss how Human Systems Integration (HSI) was applied to NASA's Constellation program, the lessons learned and best practices it revealed, and recommendations to future NASA program and project managers on how to accomplish this critical function.

"Big Data" and data-mined inferences are affecting more and more of our lives, and concerns about their possible discriminatory effects are growing. Methods for discrimination-aware data mining and fairness-aware data mining aim at keeping decision processes supported by information technology free from unjust grounds. However, these formal approaches alone are not sufficient to solve the problem. In the present article, we describe reasons why discrimination with data can and typically does arise through the combined effects of human and machine-based reasoning, and argue that this requires a deeper understanding of the human side of decision-making with data mining. We describe results from a large-scale human-subjects experiment that investigated such decision-making, analyzing the reasoning that participants reported during their task to assess whether a loan request should or would be granted. We derive data protection by design strategies for making decision-making discrimination-aware in an accountable way, grounding these requirements in the accountability principle of the European Union General Data Protection Regulation, and outline how their implementations can integrate algorithmic, behavioral, and user interface factors.

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human and machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.

A method is presented for learning the reciprocal feedforward and feedback connections required by the predictive coding model of cortical function. When this method is used, feedforward and feedback connections are learned simultaneously and independently in a biologically plausible manner. The performance of the proposed algorithm is evaluated by applying it to learning the elementary components of artificial and natural images. For artificial images, the bars problem is employed, and the proposed algorithm is shown to produce state-of-the-art performance on this task. For natural images, components resembling Gabor functions are learned in the first processing stage, and neurons responsive to corners are learned in the second processing stage. The properties of these learned representations are in good agreement with neurophysiological data from V1 and V2. The proposed algorithm demonstrates for the first time that a single computational theory can explain the formation of cortical RFs and also the response properties of cortical neurons once those RFs have been learned.

Most state-of-the-art visual attention models estimate the probability distribution of fixating the eyes in a location of the image, the so-called saliency maps. Yet, these models do not predict the temporal sequence of eye fixations, which may be valuable for better predicting the human eye fixations, as well as for understanding the role of the different cues during visual exploration. In this paper, we present a method for predicting the sequence of human eye fixations, which is learned from the recorded human eye-tracking data. We use least-squares policy iteration (LSPI) to learn a visual exploration policy that mimics the recorded eye-fixation examples. The model uses a different set of parameters for the different stages of visual exploration that capture the importance of the cues during the scanpath. In a series of experiments, we demonstrate the effectiveness of using LSPI for combining multiple cues at different stages of the scanpath. The learned parameters suggest that the low-level and high-level cues (semantics) are similarly important at the first eye fixation of the scanpath, and the contribution of high-level cues keeps increasing during the visual exploration. Results show that our approach obtains the state-of-the-art performances on two challenging data sets: 1) OSIE data set and 2) MIT data set.

In reinforcement learning (RL), a decision maker searching for the most rewarding option is often faced with the question: What is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: How can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and we describe an equivalence between the Bayesian model and temporal difference learning algorithms that have been proposed as models of RL in humans and animals. According to our view, the search for the best option is guided by abstract knowledge about the relationships between different options in an environment, resulting in greater search efficiency compared to traditional RL algorithms previously applied to human cognition. In two behavioral experiments, we test several predictions of our model, providing evidence that humanslearn and exploit structured inductive knowledge to make predictions about novel options. In light of this model, we suggest a new interpretation of dopaminergic responses to novelty.

In all parts of organisations there flourish developments of different new subsystems in areas of knowledge and learning. Over recent decades, new systems for classification of jobs have emerged both at the level of organisations and at a macro-labour market level. Recent developments in job evaluation systems make it possible to cope with the new demands for equity at work (between, for example, genders, races, physical abilities). Other systems have emerged to describe job requirements in terms of skills, knowledge and competence. Systems for learning at work and web-based learning have created a demand for new ways to classify and to understand the process of learning. Often these new systems have been taken from other areas of the organisation not directly concerned with facilitating workplace learning. All these new systems are of course closely interrelated but, in most organisations, a major problem is the severe lack of cohesion and compatibility between the different subsystems. The aim of this paper is to propose a basis for how different human resource systems can be integrated into the business development of an organisation. We discuss this problem and develop proposals alternative to integrated macro-systems. A key element in our proposition is a structure for classification of knowledge and skill to be used in all parts of the process. This structure should be used as an added dimension or an overlay on all other subsystems of the total process. This will facilitate a continued use of all existing systems within different organisations. We develop Burge's (personal communication) model for learning to show that learning is not a successive linear process, but rather an iterative process. In this way we emphasise the need for greater involvement of learners in the development of learning systems towards increased usability in a networked system. This paper is divided into two parts which are closely related. The first part gives an overview of the

Non-discrimination is considered to be a cornerstone of the human rights framework of the United Nations. Already in the UN Charter of 1945 it is stated that human rights should be promoted without discrimination as to, amongst other things, sex. This principle of non-discrimination on the ground of

Pigeons were trained on two temporal bisection tasks, which alternated every two sessions. In the first task, they learned to choose a red key after a 1-s signal and a green key after a 4-s signal; in the second task, they learned to choose a blue key after a 4-s signal and a yellow key after a 16-s signal. Then the pigeons were exposed to a…

Full Text Available Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e. tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN, which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that

Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

Full Text Available Discovering visual dynamics during human actions is a challenging task for human action recognition. To deal with this problem, we theoretically propose the multi-task conditional random fields model and explore its application on human action recognition. For visual representation, we propose the part-induced spatiotemporal action unit sequence to represent each action sample with multiple partwise sequential feature subspaces. For model learning, we propose the multi-task conditional random fields (MTCRFs model to discover the sequence-specific structure and the sequence-shared relationship. Specifically, the multi-chain graph structure and the corresponding probabilistic model are designed to represent the interaction among multiple part-induced action unit sequences. Moreover we propose the model learning and inference methods to discover temporal context within individual action unit sequence and the latent correlation among different body parts. Extensive experiments are implemented to demonstrate the superiority of the proposed method on two popular RGB human action datasets, KTH & TJU, and the depth dataset in MSR Daily Activity 3D.

Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on water using radar has not been extensively studied, unlike the case on dry ground, due to its unique challenge. Namely, not only is the radar cross section of a human on water small, but the micro-Doppler signatures are much noisier due to water drops and waves. In this paper, we first investigate whether discriminative signatures could be obtained for activities on water through a simulation study. Then, we show how we can effectively achieve high classification accuracy by applying deep convolutional neural networks (DCNN) directly to the spectrogram of real measurement data. From the five-fold cross-validation on our dataset, which consists of five aquatic activities, we report that the conventional feature-based scheme only achieves an accuracy of 45.1%. In contrast, the DCNN trained using only the collected data attains 66.7%, and the transfer learned DCNN, which takes a DCNN pre-trained on a RGB image dataset and fine-tunes the parameters using the collected data, achieves a much higher 80.3%, which is a significant performance boost.

Full Text Available Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on water using radar has not been extensively studied, unlike the case on dry ground, due to its unique challenge. Namely, not only is the radar cross section of a human on water small, but the micro-Doppler signatures are much noisier due to water drops and waves. In this paper, we first investigate whether discriminative signatures could be obtained for activities on water through a simulation study. Then, we show how we can effectively achieve high classification accuracy by applying deep convolutional neural networks (DCNN directly to the spectrogram of real measurement data. From the five-fold cross-validation on our dataset, which consists of five aquatic activities, we report that the conventional feature-based scheme only achieves an accuracy of 45.1%. In contrast, the DCNN trained using only the collected data attains 66.7%, and the transfer learned DCNN, which takes a DCNN pre-trained on a RGB image dataset and fine-tunes the parameters using the collected data, achieves a much higher 80.3%, which is a significant performance boost.

We present a learning system, socially guided exploration, in which a social robot learns new tasks through a combination of self-exploration and social interaction. The system's motivational drives, along with social scaffolding from a human partner, bias behaviour to create learning opportunities for a hierarchical reinforcement learning mechanism. The robot is able to learn on its own, but can flexibly take advantage of the guidance of a human teacher. We report the results of an experiment that analyses what the robot learns on its own as compared to being taught by human subjects. We also analyse the video of these interactions to understand human teaching behaviour and the social dynamics of the human-teacher/robot-learner system. With respect to learning performance, human guidance results in a task set that is significantly more focused and efficient at the tasks the human was trying to teach, whereas self-exploration results in a more diverse set. Analysis of human teaching behaviour reveals insights of social coupling between the human teacher and robot learner, different teaching styles, strong consistency in the kinds and frequency of scaffolding acts across teachers and nuances in the communicative intent behind positive and negative feedback.

Artificial grammars (AG) can be used to generate rule-based sequences of stimuli. Some of these can be used to investigate sequence-processing computations in non-human animals that might be related to, but not unique to, human language. Previous AG learning studies in non-human animals have used different AGs to separately test for specific sequence-processing abilities. However, given that natural language and certain animal communication systems (in particular, song) have multiple levels of complexity, mixed-complexity AGs are needed to simultaneously evaluate sensitivity to the different features of the AG. Here, we tested humans and Rhesus macaques using a mixed-complexity auditory AG, containing both adjacent (local) and non-adjacent (longer-distance) relationships. Following exposure to exemplary sequences generated by the AG, humans and macaques were individually tested with sequences that were either consistent with the AG or violated specific adjacent or non-adjacent relationships. We observed a considerable level of cross-species correspondence in the sensitivity of both humans and macaques to the adjacent AG relationships and to the statistical properties of the sequences. We found no significant sensitivity to the non-adjacent AG relationships in the macaques. A subset of humans was sensitive to this non-adjacent relationship, revealing interesting between- and within-species differences in AG learning strategies. The results suggest that humans and macaques are largely comparably sensitive to the adjacent AG relationships and their statistical properties. However, in the presence of multiple cues to grammaticality, the non-adjacent relationships are less salient to the macaques and many of the humans.

The sequencing of dance movements may be thought of as a grammar. We investigate implicit learning of regularities that govern sequences of unfamiliar, discrete dance movements. It was hypothesized that observers without prior experience with contemporary dance would be able to learn regularities that underpin structured human movement. Thirty-one adults were assigned to either an exposure or a control group. Exposure consisted of 22 grammatical 3-, 4-, and 5-movement sequences presented twice in random order; sequence duration ranged from 9 to 19 s. In a test phase, exposure and control groups identified previously unseen sequences as grammatical or ungrammatical, and rated confidence of judgment. The exposure group selected significantly more new grammatical sequences in the test phase than the control group. In addition, for the exposure group, the zero correlation criterion, wherein no relation between confidence and accuracy indicates unconscious knowledge, was satisfied. Through exposure, novice observers can learn a grammar that governs the sequencing of dance movements. This has implications for implicit learning of long sequences, working memory, and the development of expectations through exposure to contemporary dance.

Taken as a whole, this volume can be viewed as an argument for reframing learning research as a human science, one focused on interpreting learning situations and organizing for improving learning in ways that put human agency, values, and engagement with social practices at the center. Each chapter illuminates one or more elements of a human…

Patterns of topological arrangement are widely used for both animal and human brains in the learning process. Nevertheless, automatic learning techniques frequently overlook these patterns. In this paper, we apply a learning technique based on the structural organization of the data in the attribute space to the problem of discriminating the senses of 10 polysemous words. Using two types of characterization of meanings, namely semantical and topological approaches, we have observed significative accuracy rates in identifying the suitable meanings in both techniques. Most importantly, we have found that the characterization based on the deterministic tourist walk improves the disambiguation process when one compares with the discrimination achieved with traditional complex networks measurements such as assortativity and clustering coefficient. To our knowledge, this is the first time that such deterministic walk has been applied to such a kind of problem. Therefore, our finding suggests that the tourist walk c...

Learning styles vary among individuals, and understanding which instructional tools certain learning styles prefer can be utilized to enhance student learning. Students in the introductory Food Science and Human Nutrition course (FSHN 101), taught at the Univ. of Illinois at Urbana-Champaign, were asked to complete Gregorc's Learning Style…

Learning styles vary among individuals, and understanding which instructional tools certain learning styles prefer can be utilized to enhance student learning. Students in the introductory Food Science and Human Nutrition course (FSHN 101), taught at the Univ. of Illinois at Urbana-Champaign, were asked to complete Gregorc's Learning Style…

A study of the effectiveness of music and rhythm used in classroom activities as a technique for developing short-term memory for phonological learning had as subjects 25 adult Cambodian, Lao, Hmong, and Vietnamese immigrants, students in a course in English as a second language. The subjects were given a pretest of their ability to distinguish…

Objective: to investigate the process of learning on human resource management in the radiology residency program at Escola Paulista de Medicina - Universidade Federal de Sao Paulo, aiming at improving radiologists' education. Materials and methods: exploratory study with a quantitative and qualitative approach developed with the faculty staff, preceptors and residents of the program, utilizing a Likert questionnaire (46), taped interviews (18), and categorization based on thematic analysis. Results: According to 71% of the participants, residents have clarity about their role in the development of their activities, and 48% said that residents have no opportunity to learn how to manage their work in a multidisciplinary team. Conclusion: Isolation at medical records room, little interactivity between sectors with diversified and fixed activities, absence of a previous culture and lack of a training program on human resources management may interfere in the development of skills for the residents' practice. There is a need to review objectives of the medical residency in the field of radiology, incorporating, whenever possible, the commitment to the training of skills related to human resources management thus widening the scope of abilities of the future radiologists. (author)

Objective To investigate the process of learning on human resource management in the radiology residency program at Escola Paulista de Medicina - Universidade Federal de São Paulo, aiming at improving radiologists' education. Materials and Methods Exploratory study with a quantitative and qualitative approach developed with the faculty staff, preceptors and residents of the program, utilizing a Likert questionnaire (46), taped interviews (18), and categorization based on thematic analysis. Results According to 71% of the participants, residents have clarity about their role in the development of their activities, and 48% said that residents have no opportunity to learn how to manage their work in a multidisciplinary team. Conclusion Isolation at medical records room, little interactivity between sectors with diversified and fixed activities, absence of a previous culture and lack of a training program on human resources management may interfere in the development of skills for the residents' practice. There is a need to review objectives of the medical residency in the field of radiology, incorporating, whenever possible, the commitment to the training of skills related to human resources management thus widening the scope of abilities of the future radiologists. PMID:25741056

Full Text Available Objective To investigate the process of learning on human resource management in the radiology residency program at Escola Paulista de Medicina – Universidade Federal de São Paulo, aiming at improving radiologists' education. Materials and Methods Exploratory study with a quantitative and qualitative approach developed with the faculty staff, preceptors and residents of the program, utilizing a Likert questionnaire (46, taped interviews (18, and categorization based on thematic analysis. Results According to 71% of the participants, residents have clarity about their role in the development of their activities, and 48% said that residents have no opportunity to learn how to manage their work in a multidisciplinary team. Conclusion Isolation at medical records room, little interactivity between sectors with diversified and fixed activities, absence of a previous culture and lack of a training program on human resources management may interfere in the development of skills for the residents' practice. There is a need to review objectives of the medical residency in the field of radiology, incorporating, whenever possible, the commitment to the training of skills related to human resources management thus widening the scope of abilities of the future radiologists.

An intelligent human-robot interaction (HRI) system with adjustable robot behavior is presented. The proposed HRI system assists the human operator to perform a given task with minimum workload demands and optimizes the overall human-robot system performance. Motivated by human factor studies, the presented control structure consists of two control loops. First, a robot-specific neuro-adaptive controller is designed in the inner loop to make the unknown nonlinear robot behave like a prescribed robot impedance model as perceived by a human operator. In contrast to existing neural network and adaptive impedance-based control methods, no information of the task performance or the prescribed robot impedance model parameters is required in the inner loop. Then, a task-specific outer-loop controller is designed to find the optimal parameters of the prescribed robot impedance model to adjust the robot's dynamics to the operator skills and minimize the tracking error. The outer loop includes the human operator, the robot, and the task performance details. The problem of finding the optimal parameters of the prescribed robot impedance model is transformed into a linear quadratic regulator (LQR) problem which minimizes the human effort and optimizes the closed-loop behavior of the HRI system for a given task. To obviate the requirement of the knowledge of the human model, integral reinforcement learning is used to solve the given LQR problem. Simulation results on an x - y table and a robot arm, and experimental implementation results on a PR2 robot confirm the suitability of the proposed method.

A major component of reliability, safety, and mission success for space missions is ensuring that the humans involved (flight crew, ground crew, mission control, etc.) perform their tasks and functions as required. This includes compliance with training and procedures during normal conditions, and successful compensation when malfunctions or unexpected conditions occur. A very significant issue that affects human performance in space flight is human error. Human errors can invalidate carefully designed equipment and procedures. If certain errors combine with equipment failures or design flaws, mission failure or loss of life can occur. The control of human error during operation of the International Space Station (ISS) will be critical to the overall success of the program. As experience from Mir operations has shown, human performance plays a vital role in the success or failure of long duration space missions. The Department of Energy`s Idaho National Engineering and Environmental Laboratory (INEEL) is developed a systematic approach to enhance human performance and reduce human errors for ISS operations. This approach is based on the systematic identification and evaluation of lessons learned from past space missions such as Mir to enhance the design and operation of ISS. This paper describes previous INEEL research on human error sponsored by NASA and how it can be applied to enhance human reliability for ISS.

The stress response is essential to the survival of all species as it maintains internal equilibrium and allows organisms to respond to threats in the environment. Most stress research has focused on the detrimental impacts of stress on cognition and behavior. Reversal learning, which requires a change in response strategy based on one dimension of the stimuli, is one type of behavioral flexibility that is facilitated following some brief stress procedures. The current study investigated a po...

Full Text Available We asked younger and older human participants to perform computer-based configural discriminations that were designed to detect acquired equivalence. Both groups solved the discriminations but only the younger participants demonstrated acquired equivalence. The discriminations involved learning the preferences (‘like’ [+] or ‘dislike’ [-] for sports (e.g., tennis [t] and hockey [h] of four fictitious people (e.g., Alice [A], Beth [B], Charlotte [C] & Dorothy [D]. In one experiment, the discrimination had the form: At+, Bt-, Ct+, Dt-, Ah-, Bh+, Ch-, Dh+. Notice that, e.g., Alice and Charlotte are ‘equivalent’ in liking tennis but disliking hockey. Acquired equivalence was assessed in ancillary components of the discrimination (e.g., by looking at the subsequent rate of ‘whole’ versus ‘partial’ reversal learning. Acquired equivalence is anticipated by a network whose hidden units are shared when inputs (e.g., A and C signal the same outcome (e.g., + when accompanied by the same input (t. One interpretation of these results is that there are age-related differences in the mechanisms of configural acquired equivalence.

We asked younger and older human participants to perform computer-based configural discriminations that were designed to detect acquired equivalence. Both groups solved the discriminations but only the younger participants demonstrated acquired equivalence. The discriminations involved learning the preferences ["like" (+) or "dislike" (-)] for sports [e.g., tennis (t) and hockey (h)] of four fictitious people [e.g., Alice (A), Beth (B), Charlotte (C), and Dorothy (D)]. In one experiment, the discrimination had the form: At+, Bt-, Ct+, Dt-, Ah-, Bh+, Ch-, Dh+. Notice that, e.g., Alice and Charlotte are "equivalent" in liking tennis but disliking hockey. Acquired equivalence was assessed in ancillary components of the discrimination (e.g., by looking at the subsequent rate of "whole" versus "partial" reversal learning). Acquired equivalence is anticipated by a network whose hidden units are shared when inputs (e.g., A and C) signal the same outcome (e.g., +) when accompanied by the same input (t). One interpretation of these results is that there are age-related differences in the mechanisms of configural acquired equivalence.

It has been suggested that prior experiences with unpredictable/uncontrollable stressors facilitate subsequent fear learning and the development of anxiety disorders. However, animal research documents that preexposure to unpredictable stressors (USs) impede later fear conditioning with that US. These differential predictions were tested in a human experimental model of clinical anxiety. One (US-only) group was preexposed to unpredictable shocks, a second (Unpaired) group received explicitly unpaired presentations of a neutral shape and the shock, and a third (Paired) group received paired shape-shock presentations. Next, all groups received training with a novel shape, using the same shock (50% reinforcement). Fear responding was assessed through startle modulation and online shock-expectancy ratings. Results showed retarded fear learning in the unpredictable groups compared to the predictable group. We argue that prior experiences of unpredictability may still contribute to the development of clinical anxiety, by impeding adaptive fear learning and perpetuating the perception of unpredictability/uncontrollability.

Full Text Available Responses are quicker to predictable stimuli than if the time and place of appearance is uncertain. Studies that manipulate target predictability often involve overt cues to speed up response times. However, less is known about whether individuals will exhibit faster response times when target predictability is embedded within the inter-trial relationships. The current research examined the combined effects of spatial and temporal target predictability on reaction time (RT and allocation of overt attention in a sustained attention task. Participants responded as quickly as possible to stimuli while their RT and eye movements were measured. Target temporal and spatial predictability were manipulated by altering the number of: 1 different time intervals between a response and the next target; and 2 possible spatial locations of the target. The effects of target predictability on target detection (Experiment 1 and target discrimination (Experiment 2 were tested. For both experiments, shorter RTs as target predictability increased across both space and time were found. In addition, the influences of spatial and temporal target predictability on RT and the overt allocation of attention were task dependent; suggesting that effective orienting of attention relies on both spatial and temporal predictability. These results indicate that stimulus predictability can be increased without overt cues and detected purely through inter-trial relationships over the course of repeated stimulus presentations.

Responses are quicker to predictable stimuli than if the time and place of appearance is uncertain. Studies that manipulate target predictability often involve overt cues to speed up response times. However, less is known about whether individuals will exhibit faster response times when target predictability is embedded within the inter-trial relationships. The current research examined the combined effects of spatial and temporal target predictability on reaction time (RT) and allocation of overt attention in a sustained attention task. Participants responded as quickly as possible to stimuli while their RT and eye movements were measured. Target temporal and spatial predictability were manipulated by altering the number of: 1) different time intervals between a response and the next target; and 2) possible spatial locations of the target. The effects of target predictability on target detection (Experiment 1) and target discrimination (Experiment 2) were tested. For both experiments, shorter RTs as target predictability increased across both space and time were found. In addition, the influences of spatial and temporal target predictability on RT and the overt allocation of attention were task dependent; suggesting that effective orienting of attention relies on both spatial and temporal predictability. These results indicate that stimulus predictability can be increased without overt cues and detected purely through inter-trial relationships over the course of repeated stimulus presentations.

The ability in animals to count and represent different numbers of objects has received a great deal of attention in the past few decades. Cumulative evidence from comparative studies on number discriminations report obvious analogies among human babies, non-human primates and birds and are consistent with the hypothesis of two distinct and widespread mechanisms, one for counting small numbers (verbal creatures studied; results are in agreement with the hypothesis of the existence of two distinct systems for quantity discrimination in vertebrates.

Full Text Available Since the mid-1990s, the European Court of Human Rights has had before it a number of cases concerning the situation of minorities under Article 14 of the European Convention for the Protection of Human Rights - which aims to secure the enjoyment of rights and freedoms without discrimination inter alia on grounds of association with a national minority. At present, the number of similar cases pending before the Court is growing. Through an examination of cases concerning mainly the nexus between Article 11 and Article 14 as well as Article 8 and Article 14, this article seeks to identify a number of problematic aspects of the jurisprudence of the Court. This, the author argues, includes uncertainty as to when and why the Court chooses to examine Article 14; issues of cumulative violations; issues of evidence; the questionable principle of prevention; issues of indirect discrimination and last, but not least, the potential benefits of the entry into force of Protocol No. 12. To address these problems, the author concludes that there is a need for greater coherency in the positions adopted by the Court with respect to minority issues as well as a need for more legal research.

We often encounter pairs of variables in the world whose mutual relationship can be described by a function. After training, human responses closely correspond to these functional relationships. Here we study how humans predict unobserved segments of a function that they have been trained on and we compare how human predictions differ to those made by various function-learning models in the literature. Participants' performance was best predicted by the polynomial functions that generated the observations. Further, participants were able to explicitly report the correct generating function in most cases upon a post-experiment survey. This suggests that humans can abstract functions. To understand how they do so, we modeled humanlearning using an hierarchical Bayesian framework organized at two levels of abstraction: function learning and parameter learning, and used it to understand the time course of participants' learning as we surreptitiously changed the generating function over time. This Bayesian model selection framework allowed us to analyze the time course of function learning and parameter learning in relative isolation. We found that participants acquired new functions as they changed and even when parameter learning was not completely accurate, the probability that the correct function was learned remained high. Most importantly, we found that humans selected the simplest-fitting function with the highest probability and that they acquired simpler functions faster than more complex ones. Both aspects of this behavior, extent and rate of selection, present evidence that human function learning obeys the Occam's razor principle.

Two studies focusing on the development and validation of the Online Self-Regulated Learning Inventory (OSRLI) were conducted. The OSRLI is a self-report instrument assessing the human interaction dimension of online self-regulated learning. It consists of an affect/motivation scale and an interaction strategies scale. In Study 1, exploratory…

This paper reports the results of an experiment in a Computer Architecture Laboratory course classroom session, in which students were divided into two groups for interaction both with a hinting e-learning system and with human teachers generating hints. The results show that there were high learning gains for both groups, demonstrating the…

Historically, medical students who are lesbian, gay, bisexual or transgendered (LGBT) report higher rates of social stress, depression, and anxiety, while LGBT patients have reported discrimination and poorer access to healthcare. The objectives of this study were: (1) to assess if medical students have perceived discrimination in their learning environment and; (2) to determine self-reported comfort level for caring for LGBT patients. Medical students at the University of Ottawa (N = 671) were contacted via email and invited to complete a confidential web-based survey. Response rate was 15.4% (103/671). This included 66 cis-gender heterosexuals (64.1%) and 37 LGBT students (35.9%). Anti-LGBT discrimination had been witnessed by 14.6% and heterosexism by 31.1% of respondents. Anti-LGBT discrimination most often originated from fellow medical students. Respondents who self-identified as LGBT were more likely to have perceived heterosexism (favoring opposite-sex relationships) (OR = 8.2, p LGBT discrimination (OR = 6.6, p = 0.002). While half of LGBT students shared their status with all classmates (51.4%), they were more likely to conceal this from staff physicians (OR = 27.2, p = 0.002). Almost half of medical students (41.7%) reported anti-LGBT jokes, rumors, and/or bullying by fellow medical students and/or other members of the healthcare team. Still, most respondents indicated that they felt comfortable with and capable of providing medical care to LGBT patients (≥83.5%), and were interested in further education around LGBT health issues (84.5%). Anti-LGBT discrimination and heterosexism are noted by medical students, indicating a suboptimal learning environment for LGBT students. Nonetheless, students report a high level of comfort and confidence providing health care to LGBT patients.

E-learning and digital learning materials are becoming more and more popular. Therefore we investigated the value of digital learning material for academic education in Human Nutrition. Five digital modules were developed focusing on for example design, analysis and interpretation of nutrigenomics

This investigation assessed the effectiveness of using Collaborative Learning Assessment through Dialogue (CLAD) (Fitch & Hulgin, 2007) with students in undergraduate human development courses. The key parts of CLAD are student collaboration, active learning, and altering the role of the instructor to a guide who enhances learning opportunities.…

Implementing service learning into college courses has been shown to have positive benefits for both students and community members; however, service learning has not been largely evaluated in the literature on human sexuality courses. Thus, the purpose of the current study was to design, implement, and evaluate a service learning project in a…

Understanding what happens when teachers embrace digital media for literacy learning is critical to realizing the potential of learning in the digital era. This article examines some of the ways that a high school teacher and his students leverage digital technologies for literacy learning in their humanities classrooms. The author introduces the…

Policy makers and researchers are increasingly showing interest in lifelong learning due to a rising unemployment rate in recent years. Much attention has been paid to determinants and benefits of lifelong learning but not to the impact of social capital on lifelong learning so far. In this article, we study how social and human capital can…

Implementing service learning into college courses has been shown to have positive benefits for both students and community members; however, service learning has not been largely evaluated in the literature on human sexuality courses. Thus, the purpose of the current study was to design, implement, and evaluate a service learning project in a…

Understanding what happens when teachers embrace digital media for literacy learning is critical to realizing the potential of learning in the digital era. This article examines some of the ways that a high school teacher and his students leverage digital technologies for literacy learning in their humanities classrooms. The author introduces the…

E-learning and digital learning materials are becoming more and more popular. Therefore we investigated the value of digital learning material for academic education in Human Nutrition. Five digital modules were developed focusing on for example design, analysis and interpretation of nutrigenomics r

Policy makers and researchers are increasingly showing interest in lifelong learning due to a rising unemployment rate in recent years. Much attention has been paid to determinants and benefits of lifelong learning but not to the impact of social capital on lifelong learning so far. In this article, we study how social and human capital can…

Speech and vocal impairments characterize many neurological disorders. However, the neurogenetic mechanisms of these disorders are not well understood, and current animal models do not have the necessary circuitry to recapitulate vocal learning deficits. We developed germline transgenic songbirds, zebra finches (Taneiopygia guttata) expressing human mutant huntingtin (mHTT), a protein responsible for the progressive deterioration of motor and cognitive function in Huntington's disease (HD). Although generally healthy, the mutant songbirds had severe vocal disorders, including poor vocal imitation, stuttering, and progressive syntax and syllable degradation. Their song abnormalities were associated with HD-related neuropathology and dysfunction of the cortical-basal ganglia (CBG) song circuit. These transgenics are, to the best of our knowledge, the first experimentally created, functional mutant songbirds. Their progressive and quantifiable vocal disorder, combined with circuit dysfunction in the CBG song system, offers a model for genetic manipulation and the development of therapeutic strategies for CBG-related vocal and motor disorders.

The present need to rapidly identify severely irradiated individuals in mass-casualty and population-monitoring scenarios prompted an evaluation of potential protein biomarkers to provide early diagnostic information after exposure. The level of specific proteins measured using immunodiagnostic technologies may be useful as protein biomarkers to provide early diagnostic information for acute radiation exposures. Herein we present results from on-going studies using a non-human primate (NHP) 6-Gy X-rays ( 0.13Gymin{sup -1}) whole-body radiation model. Protein targets were measured by enzyme-linked immunosorbent assay (ELISA) in blood plasma before, 1, and 2 days after exposure. Exposure of 10 NHPs to 6 Gy resulted in the up-regulation of plasma levels of (a) p21 WAF1/CIP1, (b) interleukin 6 (IL-6), (c) tissue enzyme salivary {alpha}-amylase, and (d) C-reactive protein. Data presented show the potential utility of protein biomarkers selected from distinctly different pathways to detect radiation exposure. A correlation analysis demonstrated strong correlations among different combinations of four candidate radiation-responsive blood protein biomarkers. Data analyzed with use of multivariate discriminant analysis established very successful separation of NHP groups: 100% discrimination power for animals with correct classification for separation between groups before and 1 day after irradiation, and 95% discrimination power for separation between groups before and 2 days after irradiation. These results also demonstrate proof-in-concept that multiple protein biomarkers provide early diagnostic information to the medical community, along with classical biodosimetric methodologies, to effectively manage radiation casualty incidents.

Virtual humans are becoming an easily available and popular component of multimedia learning that are often used in online learning environments. There is still a need for systematic research into their effectiveness. The current study investigates the positioning of a virtual human's gestures when guiding the learner through a multimedia…

Virtual humans are becoming an easily available and popular component of multimedia learning that are often used in online learning environments. There is still a need for systematic research into their effectiveness. The current study investigates the positioning of a virtual human's gestures when guiding the learner through a multimedia…

A new electrochemical biosensor based on the human serum albumin/graphene oxide/3-aminopropyl-triethoxysilane modified indium tin oxide electrode (ITO/APTES/GO/HSA) has been developed for the discrimination of tryptophan (Trp) enantiomers.The electrode has been characterized by scanning electron microscopy (SEM) and electrochemical techniques. The electrochemical behaviors of the enantiomeric pairs (D- and L-Trp) at the ITO/APTES/GO/HSA electrode have been investigated by cyclic voltammetry in the concentration range of 0.10-1.0 mM. A clear separation between the oxidation peak potentials of D- and L-Trp, at 0.86 and 1.26 V, respectively, has suggested that the ITO/APTES/GO/HSA electrode can be used as an electrochemical biosensor for the discrimination of Trp enantiomers. In order to find the percentage of an enantiomeric form of tryptophan in a mixture, the ITO/APTES/GO/HSA electrode is used for the simultaneous detection of D- and L-Trp which showed that the percentage of one enantiomeric form can be easily measured in the presence of the other.

Genetic testing can not only provide information about diseases but also their prevalence in ethnic, gender, or other vulnerable populations. While offering the promise of significant therapeutic benefits and serving to highlight our commonality, genetic information also raises a number of sensitive human rights issues touching on identity and the perception thereof, as well as the possibility of discrimination and social stigma. It stands to reason that the results of individual screenings could haplessly be used to make general assumptions about entire ethnic or gender groups. In this manner, genetic information can directly influence identity by impacting and perhaps even reframing conceptions of group rights and dimensions of self-identification, thus importing constitutional scrutiny on questions of dignity and discrimination in particular. Is there a risk of collective stigmatization deriving from discrete testing of self-identified individuals? Would such stigmatization impinge on individual dignity by the exogenous imposition of ethnic or gender/sexual identity? If so, what norms can most adequately respond if and when individual and group interests diverge? These questions are examined from a comparative perspective.

Cooperative behaviour lies at the very basis of human societies, yet its evolutionary origin remains a key unsolved puzzle. Whereas reciprocity or conditional cooperation is one of the most prominent mechanisms proposed to explain the emergence of cooperation in social dilemmas, recent experimental findings on networked Prisoner's Dilemma games suggest that conditional cooperation also depends on the previous action of the player-namely on the 'mood' in which the player is currently in. Roughly, a majority of people behave as conditional cooperators if they cooperated in the past, whereas they ignore the context and free ride with high probability if they did not. However, the ultimate origin of this behaviour represents a conundrum itself. Here, we aim specifically to provide an evolutionary explanation of moody conditional cooperation (MCC). To this end, we perform an extensive analysis of different evolutionary dynamics for players' behavioural traits-ranging from standard processes used in game theory based on pay-off comparison to others that include non-economic or social factors. Our results show that only a dynamic built upon reinforcement learning is able to give rise to evolutionarily stable MCC, and at the end to reproduce the human behaviours observed in the experiments.

Full Text Available Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference; the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

This study evaluated the sources of fecal contamination in different river catchments, using a combination of microbial source tracking tools, for human, ruminant, ovine and bovine livestock, in order to define appropriate water management strategies. Every source of waterway pollution was evaluated in river water samples from one urban river catchment and two important farming regions in New Zealand. Fecal pollution was initially measured by testing Escherichia coli and evaluating the presence of human- and ruminant-associated DNA markers of Bacteroidales (BiAdo, BacHum-UCD, BacH, and BacR) and human and ruminant fecal sterols/stanols ratios. Then specific fecal pollution sources were assessed with previously reported quantitative PCR assays targeting human-, bovine-, and ovine-specific viruses: human adenoviruses (HAdV), human JC polyomaviruses, bovine polyomaviruses (BPyV), and ovine polyomaviruses (OPyV). High level of ruminant fecal contamination was detected all over the farming areas, whereas no ruminant sources were identified in the urban river sampling sites. BacR was the most frequently observed ruminant marker and OPyV and BPyV allowed the identification of ovine and bovine fecal sources. The human fecal viral marker (HAdV) was the most frequently observed human marker, highly abundant in the urban sites, and also present in farming areas. This is the first study using simultaneously the ovine and the bovine viral markers to identify and quantify both bovine and ovine fecal pollution.

In this study we present a novel set of discrimination-based indicators of language processing derived from Naive DiscriminativeLearning (ndl) theory. We compare the effectiveness of these new measures with classical lexical-distributional measures—in particular, frequency counts and form similarity measures—to predict lexical decision latencies when a complete morphological segmentation of masked primes is or is not possible. Data derive from a re-analysis of a large subset of decision latencies from the English Lexicon Project, as well as from the results of two new masked priming studies. Results demonstrate the superiority of discrimination-based predictors over lexical-distributional predictors alone, across both the simple and primed lexical decision tasks. Comparable priming after masked corner and cornea type primes, across two experiments, fails to support early obligatory segmentation into morphemes as predicted by the morpho-orthographic account of reading. Results fit well with ndl theory, which, in conformity with Word and Paradigm theory, rejects the morpheme as a relevant unit of analysis. Furthermore, results indicate that readers with greater spelling proficiency and larger vocabularies make better use of orthographic priors and handle lexical competition more efficiently. PMID:28235015

The authors present a classroom experiment designed to illustrate key concepts of third-degree price discrimination. By participating as buyers and sellers, students actively learn (1) how group pricing differs from uniform pricing, (2) how resale between buyers limits a seller's ability to price discriminate, and (3) how preventing price…

Honey bees (Apis mellifera, worker) were trained to discriminate between two random gratings oriented perpendicularly to each other. This task was quickly learned with vertical, horizontal, and oblique gratings. After being trained on perpendicularly-oriented random gratings, bees could discriminate

Tracking can be seen as an online learning problem, where the focus is on discriminating object from background. From this point of view, features play a key role as the tracking accuracy depends on how well the feature distinguish object and background. Current discriminative trackers use tradition

Tracking can be seen as an online learning problem, where the focus is on discriminating object from background. From this point of view, features play a key role as the tracking accuracy depends on how well the feature distinguish object and background. Current discriminative trackers use tradition

Honey bees (Apis mellifera, worker) were trained to discriminate between two random gratings oriented perpendicularly to each other. This task was quickly learned with vertical, horizontal, and oblique gratings. After being trained on perpendicularly-oriented random gratings, bees could discriminate

Relative olfactory bulb size with respect to telencephalic hemispheres (olfactory ratio) was measured in five species of hummingbirds. Trochiliformes were found to be next to last among 25 avian orders with respect to olfactory bulb development. One hummingbird species, the White-vented Violetear (Colibri serrirostris), was trained in a successive go/no-go discrimination task, and learned to feed or not to feed from a container dependent on the olfactory stimuli associated with it. Test birds learned to discriminate amyl acetate vs. turpentine essence, jasmine essence vs. lavender essence, eucalyptus essence vs. no odor, beta-ionone vs. no odor, carvone vs. eucalyptol. In contrast, 1-phenylethanol vs. beta-ionone discrimination, two odorants which appear similar to humans, was unsuccessful. Using a similar procedure, attempts were made to condition a White-vented Violetear and a Versicolored Emerald (Amazilia versicolor) to magnetic stimuli. The birds were unable to discriminate between a normal field and an oscillating field (square wave, 1 Hz, amplitude +/- 0.40 G).

One of the fundamental requirements for an artificial hand to successfully grasp and manipulate an object is to be able to distinguish different objects' shapes and, more specifically, the objects' surface curvatures. In this study, we investigate the possibility of enhancing the curvature detection of embedded tactile sensors by proposing a ridged fingertip structure, simulating human fingerprints. In addition, a curvature detection approach based on machine learning methods is proposed to provide the embedded sensors with the ability to discriminate the surface curvature of different objects. For this purpose, a set of experiments were carried out to collect tactile signals from a 2 \\times 2 tactile sensor array, then the signals were processed and used for learning algorithms. To achieve the best possible performance for our machine learning approach, three different learning algorithms of Na\\"ive Bayes (NB), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) were implemented and compared ...

Our early life experience has a strong influence on our actions in later life. Humans today are just starting to re-learn, collectively, how to treat Earth with the respect that it deserves and that is needed for our offspring to inherit a decent home. However, we still have a long way to go to instill in people at large the ethics, knowledge and skills necessary to ensure a healthy journey for humanity on spaceship. The experience of early upbringing, of schooling and of everyday life is probably the only path strong enough to develop in people a strong desire for ethical behaviour towards their environment. The problem is that the measures taken today to ensure the development of ethical behaviours in the population at large are woefully inadequate. At best, western school programmes contain a few lessons devoted to the environment, and even then they usually just pay lip service to the basics of the environment; they rarely aim to instill skills and knowledge in order to understand and care deeply for the environment. My presentation will suggest some practical ways to help communities build ethical frameworks and strategies to guide and generate tools, methods and activities that guide young people (pupils, students, scholars, researchers) to toward more ethical behaviours regarding their environment and their communities. Examples might include: - Developing geoethical dimensions of internships, in all areas; - Designing, testing and running simulation/games+debriefing providing a rich affective-cognitive context for grappling with geoethical problems- eg, FISH BANKS, KEEP COOL. - Pressuring governments to make geoethics, environmental care and climate change understanding central components of (almost) all educational programmes (in, eg, history, language, business, law, medicine, etc). - Subsidizing environmental-care summer schools for families and teachers at all levels. - Etc. One of my actions is founding a academic journal in the area, maybe with the

The case law of the CoJ and the ECtHR on discrimination on ground of nationality differs, in particular since the latter Court does not use the concept of indirect discrimination. Furthermore for the CoJ being an EU citizen is a relevant argument in the objective justification of discrimination wher

Measurements were made on the basis of the osteological collection of the chair for anthropology, Moscow State University (70 cases), and on the basis of a series of skeletons (10 cases) from among burial places of the Novospassk Monastery (males aged above 18-20). Eleven sizes of Martin program (length, diaphysis circumference and epiphysis width) were fixed onto the humerus, radial, femoral and shin bones. Simultaneously, the development of the osseous relief elements in the above bones (a total of 18 signs in each skeleton) was evaluated by Fedosova program. The data was processed by SPSS. Discriminative analysis was used as a basis to work out a diagnostic model that can be used to determine a somatotype by the humerus, radial and femoral bones. The classification accuracy is 75%. The method should be applied in those cases, when the appropriate bones are available. If the available combination of bones is different from the above, the routine method is recommended for use, i.e. determination of a somatotype by the skeleton massiveness.